ABSTRACT
The past decade has witnessed high volatility in the global macroeconomic conditions
and has shown how the banking system is exposed to a variety of risks which can
assume systemic dimensions and can affect the financial stability of a country
adversely. Given the importance of financial stability, organizations like International
Monetary Fund (IMF), World Bank, Bank for International Settlements (BIS) and the
Central banks across the globe are striving to evolve a robust Macro Prudential and
Systemic risk assessment framework. Macro stress testing is an integral element of
this exercise. In the Indian context also, Macro stress testing has evolved over the last
few years; however, given the advancements in this area across the globe, the research
is still in a very nascent stage. Against this backdrop, our study is an attempt to
contribute to the ongoing research efforts in this area and provide a reference point for
reassessing and reviewing the existing macro stress testing practices in the Indian
context. We propose to modify the existing macro stress testing model for credit risk
as developed by Reserve Bank of India in terms of endogenous variable selection,
calibration of stress testing scenarios and modification of the macro stress testing
model. In the dissertation, Top-down approach of stress testing has been adopted for
the Indian banks using quarterly data pertaining to the time period 1996Q2 to
2016Q4. The macroeconomic variables employed for the study are GDP, CPI,
Exchange rate, Oil, Market Capitalisation of NSE, Short-term interest rate and Long
term interest rate. For the empirical analysis, Vector Error Correction Model (VECM)
technique has been employed to investigate the dynamic impact of changes in the
macroeconomic variables on the Default Ratio which has been taken as a Credit Risk
indicator. Wald Test, Granger Causality and Toda Yamamoto test have been
vii
employed to investigate the short term relationship between the variables. The
constructed model has been subjected to stress tests by employing Impulse Response
Function (IRF) and Variance Decomposition Analysis (VDA). The results suggest a
long run relationship running between Default rate (DR) and all the variables (GDP,
CPI, Exchange rate, Oil, Market Capitalisation of NSE, Short-term interest rate and
Long term interest rate). Wald test results suggest a weak causality running from CPI
to DR. Pair wise Granger Causality tests show a unidirectional causality running from
DR to Market Capitalisation of NSE and a weak unidirectional causality running from
CPI to DR. However, the most robust of the three tests Toda Yamamoto test results
provide evidence of bidirectional causality running from CPI to DR and vice-versa, a
unidirectional causal relationship running from DR to long term interest rate and DR
and Oil. The IRFs support the existing theory that initially with an increase in GDP,
DR falls, however in the long run, an increase in GDP may lead to increased default.
With respect to CPI and long term interest rate, in the long run, DR is likely to
increase with increase in CPI and long term interest rate. The VDA results
substantiate the significant role played by interest rates (both short term interest rates
and long term interest rates) and CPI in accounting for fluctuations in DR in the long
run. Such a study can provide useful inputs for regulators and policy makers in
enhancing and developing the existing stress testing framework and make it more
inclusive and robust for banks which are a dominant component of the Indian
financial system and core of our macroeconomic policy.
viii
CHAPTER 1
Introduction
Banks are the most important financial intermediaries in a financial system and the
financial crisis of 2007-08 has shown that banks are exposed to a variety of risks
which can assume systemic dimension at the time of stress and can further impact the
financial stability and economic growth of a country. Thereby, it becomes very
important for us to understand the macro perspective of the impact of crisis on the
banking system.
Last few years have witnessed high volatility in the global macroeconomic conditions
and in response to that, banking regulators have evolved sophisticated risk
measurement and management techniques pertaining to the banking sector but the
global financial crisis of 2007-08 exposed the weaknesses of such techniques. These
have had important repercussions on the global financial stability. Financial systems
across the globe are now developing prudential paradigms to deal with such
weaknesses. Given the dynamic financial environment, financial stability has become
the most important area of concern (Wyman, 2015) and Stress Testing is an integral
component of assessing financial stability. It is an important technique for quantifying
the vulnerabilities in a financial system. In the given backdrop, it is very important to
understand further, the concept of Stress Testing as an important tool for evaluating
financial stability in the financial systems.
1
1.1.
Stress Testing: An Overview
There have been concerted efforts made by several financial organisations like the
International Monetary Fund (IMF), World Bank, Bank for International Settlements
(BIS) and Central banks across the globe to ensure the financial stability of their
respective countries. Financial stability analysis aims at building a framework that
enables the understanding of the risks and vulnerabilities of the financial systems and
a systematic review of the possible sources of risks along with their magnitude to
assess the impact of such risks (Henry and Kok, 2013; Quagliariello, 2009). Financial
stability is difficult to define and measure because it involves complex interactions
among the various elements of the financial system and also a high degree of
interdependence between the economy and the financial system which may also be
further complicated by its cross-border nature (Gadanecz and Jayaram, 2009).
However, researchers have made an attempt to explain the concept of financial
stability and capture the features of the financial system stability.
ECB (2007) has defined financial stability as ‘A condition in which the financial
system – comprising financial intermediaries, markets and market infrastructure – is
capable of withstanding shocks and the unravelling of financial imbalances, thereby
mitigating the likelihood of disruptions in the financial intermediation process which
are severe enough to significantly impair the allocation of savings to profitable
investment opportunities’.
Within this purview, it is important to prepare a comprehensive framework for
financial stability analysis and evaluate the linkages between financial system stability
and macro economy. One of the most important mechanisms for assessing financial
2
stability is macro prudential analysis. According to IMF (2001), Macro prudential
analysis is ‘A key building block of any policy framework on vulnerability analysis. It
is a methodological tool that helps quantify and qualify the soundness and
vulnerabilities of financial systems’ (Ingves, 2001). Sundarajan et al. (2002) explain
Macro prudential analysis as ‘The assessment and monitoring of the strengths and
vulnerabilities of financial system’.
Macro prudential analysis involves use of
quantitative information on the financial system and qualitative information on the
institutional and regulatory framework to analyse the relationship between
macroeconomic variables and Financial Soundness Indicators (FSIs) (Cihak, 2004;
Roy and Bhattacharya, 2011). It focuses on the analysis of stability of financial
systems as a whole in contrast to micro prudential analysis which deals with the
analysis of individual financial institutions (Sundarajan et al., 2002).
One of the most important functions of macro prudential supervision is the
identification and assessment of systemic risks. Systemic Risk can be defined as the
‘risk that the financial instability would become so widespread that the functioning of
a financial system would be impaired to the point where economic growth and
welfare would suffer materially’ (Henry and Kok, 2013). A report by the Group of
Ten (2001) on ‘Consolidation in the financial sector’ defines systemic risk as the ‘risk
that an event will trigger a loss of economic value or confidence in a substantial
system that can probably have significant adverse effects on the real economy’.
ECB, in its Financial Stability Report (2010), identifies four broad approaches for the
analytical models with which systemic risks and instability can be assessed. First,
‘Coincident indicators’ which measure the current state of financial system instability;
3
second, ‘Early Warning models’ which enable to identify the emerging imbalances
and signals that may lead to crisis; third, ‘Macro-stress testing models’ that can be
employed to assess the resilience of the financial system and fourth, ‘Contagion and
Spill over models’ that assess the transmission of instability among the financial
markets and financial intermediaries.
Hence, one of the key elements of macro prudential analysis and systemic risk
assessment is stress testing. Stress testing refers to ‘assessing the impact of a rare but
plausible shock to the financial system’ (Howard, 2008). However, it is not a precise
tool that can be used with scientific accuracy. It is an analytical technique that can
enable us to estimate the exposure to a particular event (Jones et al., 2004). In simple
words, stress testing is –what if thinking- conducted in a structured manner (FSR, Dec
2010). It is an investigation where a bank’s financial health is stressed by an adverse
shock and tested to quantify how much deterioration might occur in case of such an
adverse shock (Kearns, 2004). It is one of the tools employed to assess the
vulnerabilities of portfolios and markets to abnormal events (Blaschke, 2001) and an
important mechanism to assess the robustness of the financial architecture.
Macro stress testing models have become an integral part of central banks’ systemic
risk assessment tools as part of macro prudential policies. Given the importance of
macro prudential analysis and stress testing, IMF and World Bank have instituted the
Financial Sector Assessment program (FSAP) which endeavours to identify
vulnerabilities in the financial systems of the member countries. The main objective
of FSAP is to help strengthen the financial systems and enhance their resilience to
potential financial crisis. Stress testing is one of the key components of FSAP
4
(Blaschke, 2001; Moretti et al., 2008). Chapter 2 gives a detailed review of the
literature available on this subject.
1.2.
Indian Banking System
Banking sector is the core of Indian financial sector and it has seen several
developments over the past few decades. If the landmark changes in the Indian
banking sector in the last five decades are examined, these can be classified in three
phases: nationalisation of banks in 1969, economic and banking sector reforms in
early 1990s; and high growth phase of banks in 2000s (Dash and Ahuja, 2016). The
financial sector reforms of 1990s lead to a significant transformation in the Indian
economy and further reforms lead to effective implementation of prudential and
regulatory norms.
The organized banking sector in India comprises of Scheduled and Non-Scheduled
banks. A Scheduled bank is a bank that is listed under the Second Schedule of the
RBI Act, 1934. As on March 31, 2017, the Indian Banking system comprised of 27
Public Sector Banks (comprising of 6 SBI and Associates, 21 Nationalised Banks
(including IDBI Bank); 21 Private Sector Banks and 49 Foreign Banks (Report on
Trend and Progress of Banking in India - Table V.6). However, due to the merger of
SBI with its associates and merger of Bhartiya Mahila Bank with SBI effective April
1, 2017, the number of banks has reduced. As of May 31, 2018, there are 21 Public
Sector Banks (including SBI which is a single merged entity now and 20 Nationalised
Banks (including IDBI); 21 Private Sector Banks and 45 Foreign Banks. Apart from
these, there are Small Finance Banks (for e.g. Ujjivan Small Finance Bank Ltd.,
Equitas Small Finance Bank Ltd., FINCARE Small Finance Bank Ltd. etc.);
5
Payments Banks (for e.g. Paytm Payments Bank Ltd, Fino Payments Bank Ltd., Jio
Payments Bank Ltd. etc.); Local Area Banks (for e.g. Coastal Local Area Bank Ltd,
Subhadra Local Area Bank Ltd., Krishna Bhima Samruddhi Local Area Bank Ltd.);
State
Co-operative
Banks
and
Regional
Rural
Banks.
(https://www.rbi.org.in/commonman/english/scripts/banksinindia.aspx#rrb).
Over the past few decades, the macroeconomic environment in which banks have
functioned has changed significantly. The financial recession of 2007-08 exposed the
vulnerability of large banks to downsides in the economy. Given the importance of
Banking in the Indian financial system and the important role they play in the
financial stability, it is very important to assess and review the models employed to
measure the resilience of these banks to financial turmoil.
1.3.
Problem Statement
Over the past few years, especially after the global financial recession of 2009,
Systemic risk and Macro Stress testing have evolved as important areas of research.
Macro stress testing is a forward looking exercise and is an integral element in the
regular macro prudential assessments of central banks. It has proved to be a useful
instrument to assess the resilience of banks to the adverse global developments. The
macro stress testing framework in the Indian context has also evolved over the last
few years. Initially, only micro stress testing was performed at the central bank (RBI)
level (FSR, March 2010); but later, macro stress testing was also incorporated in the
study of the resilience of the Indian banking system in the Financial Stability Report
published by RBI (FSR, Dec, 2010). However, the progress in this area is not as much
as has been in the case of many countries across the globe. Against this backdrop, it is
6
important to analyse and review the existing macro stress testing practices across the
globe which can provide us a reference point for reassessing and reviewing the macro
stress testing in the Indian scenario. There is a lack of sufficient research in this area
in the Indian context. Most of the papers reviewed do not make an attempt to capture
the potential information that may be inherent in a larger macroeconomic data set as
these studies take into account very few macroeconomic variables. The risk models
have not been able to capture the different aspects of macro economy that affect the
banking system.
Given the ever changing dynamic environment and a need to incorporate more factors
that affect the banking system, we propose to modify the existing macro stress testing
model as developed by RBI in terms of endogenous variable selection, calibration of
stress testing scenarios and modification of the macro stress testing model. We aim to
include a number of macroeconomic variables that affect the credit risk in the Indian
banking system and finalise the variables.
1.4.
Contribution of the Study
The study intends to make an important contribution to the existing literature in terms
of inclusion of more aspects that affect the credit risk pertaining to the banking sector.
It is an attempt to contribute to the ongoing macro prudential research efforts and also
facilitate early detection of signals of financial vulnerabilities. It is aimed to take a
wider set of macroeconomic variables and capture the dynamics of the ever changing
financial environment supported with a robust modelling framework involving a
variety of econometric techniques which will help us validate the robustness of our
results. Autoregressive approach is applied to establish the relationship between credit
7
risk and macroeconomic indicators. Such an analysis will enable us to have a deeper
understanding of the key determinants of credit and will provide useful information to
explain the resilience of Indian banking system in terms of credit risk. This study can
also provide inputs for improving the macro stress testing models further by
incorporating endogenous factors as well.
Banking is a dominant component of the Indian Financial system and is the core of
our macroeconomic policy. Therefore, such a study on the Indian Banking System can
provide useful inputs for regulators and policy makers in enhancing and developing
the existing stress testing framework and make it more inclusive and robust.
1.5.
Organisation of the Dissertation/ Intended Chapterisation
The thesis has been organised into six chapters
Chapter 1 - Introduction: Chapter 1 is the introduction to the study that includes an
overview of the concept of stress testing, a brief write up on the Indian banking
system which provides a backdrop against which this study has been conducted. It
further includes the problem statement and the contribution of the study. Finally it
lays down the chapterisation of the research.
Chapter 2 – Literature Review: Chapter 2 presents a comprehensive review of the
literature available in the area pertaining to the conceptual framework of macro stress
testing along with the various elements of stress testing. It further covers the risks
covered under stress test and the relationship between stress testing and Basel. It
helps us to understand geographically, the practices pertaining to stress testing
followed by banks across the globe with special focus on the literature pertaining to
8
the Indian context. It also delves into the empirical studies adopted in the area
emphasizing on methodologies adopted for the study.
Chapter 3 – Research Objectives: Chapter 3 provides the research gap that has been
derived through an extensive review of literature along with the research questions
and objectives of the study.
Chapter 4 Research Methods and Procedures: Chapter 4 discusses the scope of the
study along with the research procedures to be adopted for our research. It includes
the sources of data collection, period of study, operationalisation of variables and a
description of the credit risk models that have been employed for research.
Chapter 5 – Data Analysis – Estimation and Results: Chapter 5 presents the results
of the econometric analysis along with the discussions in light of the theoretical
underpinnings laid down in the literature review. It lays down the steps for the
construction of the macroeconomic credit risk model in the Indian context followed
by the implementation of stress testing methodology on the given model. The
robustness and stability of the model is also ascertained in this chapter.
Chapter 6 - Summary and Conclusion: Chapter 6 summarizes the major findings of
the study and the conclusion along with the contribution of the study in the present
context. It also presents the Implications for future research and the Limitations of the
study.
Chapter 6 is followed by References and Annexures..
9
CHAPTER 2
Literature Review
Stress testing was introduced in 1999 as part of the Financial Stability Assessment
Programme (FSAP) which was a joint initiative of IMF and World Bank to measure
the risk exposure of the financial system to severe but plausible shocks. Since then,
IMF and World Bank have emphasized the importance of stress testing with respect to
systemic risk assessment and financial stability modelling. Thereafter, several studies
have been published globally, both theoretical and empirical, which attempt to
quantify the potential impact of the adverse events on the financial system.
The financial crisis of 2007-08 highlighted the importance of stress testing as a
diagnostic tool, but at the same time, revealed the weaknesses of the practice as it
failed to capture the extent of the risks. It showed how relatively small losses can get
magnified into systemic dimensions and destabilise the financial systems the world
over. Over the last few years, stress testing has assumed an important role in the risk
management domain. Lot of research has been done in this area. However, there is
still a need to address the inherent challenges existing in the present stress testing
techniques and re-assess the prevalent practices. Therefore, it becomes important to
extensively review the literature available in this area and examine the areas that need
to be re-examined. This section presents the literature available on the subject of
stress testing.
10
2.1.
Conceptual Framework/ Theoretical Background
This section provides an overview of the concept of stress testing. To examine the
subject in detail, it is important to first understand the term ‘stress testing’.
2.1.1. Definition of Stress Testing
The Committee on the Global Financial System (2005) has defined stress testing as “a
risk management tool used to evaluate the potential impact on a firm of a specific
event and/or movement in a set of financial variables. Accordingly, it is used as an
adjunct to statistical models like Value at Risk (VaR).”
A paper on Principles and Practices of Macro financial stress testing by Oura and
Schumacher (2012) defines stress testing as “Stress testing is a technique that
measures the vulnerability of a portfolio, an institution, or an entire financial system
under different hypothetical events or scenarios. It is a quantitative - what if exercise,
estimating what would happen to capital, profits, cash flows, etc. of individual
financial firms or the system as a whole if certain risks were to materialize.” It
further states that stress testing typically evaluates two aspects of the performance of
the financial institutions- solvency and liquidity.
Jobst et al. (2013) define stress testing as “a forward looking technique that attempts
to measure the sensitivity of a portfolio, an institution, or even an entire financial
system to events that have a very small probability of occurrence but which have
significant impact if they occur.”
11
Blaschke et al. (2001) state that “Stress testing is a process that includes i)
identification of specific vulnerabilities or areas of concern; (ii) construction of a
scenario; (iii) mapping the outputs of the scenario into a form that is usable for an
analysis of financial institutions’ balance sheets and income statements; (iv)
performing the numerical analysis, (v) considering any second round effects; and (vi)
summarizing and interpreting the results.”
A definition by Marcelo et al. (2008) suggests that Stress test is “a set of techniques,
tools or, procedures used by either individual institutions or supervisory authorities
to gauge the financial condition of the system under examination.”
Jones et al. (2004) state that “Stress testing can be used to assess a variety of risks,
including market risk (the possibility of losses from changes in prices or yields),
credit risk (potential for losses from borrower defaults or non performance on a
contract), and liquidity risk (the possibility of depositor runs or losses from assets
becoming illiquid.”
Summing up the various definitions of Stress testing, it can be stated that ‘Stress
Testing is a forward looking technique to assess the impact of a rare but plausible
shock to the financial system and it enables us to examine the vulnerabilities present
in a portfolio, financial institutions or financial system as a whole under different
hypothetical events or scenarios’.
Theoretically, the determinants of the resilience of the financial sector depends on two
broad sources: Micro - which are the bank specific factors like individual risk
12
exposure, operating strategies etc. and Macro - which includes GDP growth rate,
unemployment, interest rates, exchange rates etc (Clair, 2004). In line with this, the
objectives of stress testing can be examined with respect to the following two
categories:
a) Micro Prudential Stress testing, also called Portfolio Level Stress Testing or
Supervisory Stress Testing involves periodic assessment of the financial
soundness of individual institutions under adverse economic conditions. An
example of Micro Prudential Stress Testing is the Comprehensive Capital
Assessment Review (CCAR) conducted by United States (Jobst et al., 2013;
Blaschke, 2001). The liquidity ratios in context of Basel III emphasised micro
stress testing as an integral part of the regulatory framework (Oura and
Schumacher, 2012).
b) Macro Prudential Stress Testing, also called as Aggregate Stress Testing,
System Focused Stress Testing or Surveillance Stress Testing, is aimed at
assessing
system-wide
resilience
to
shocks
from
the
macroeconomic
environment; the main focus being on identifying potential threats to overall
financial stability (Jobst et al., 2013). Macro prudential stress testing takes a
holistic view of the complete financial system in terms of the assessment and
monitoring of the strengths and vulnerabilities of the financial systems. It
incorporates macro-economic and market based data, quantitative and structural
information and Financial Soundness indicators (FSI’s) which are the core
indicators promoted by IMF to measure financial sector vulnerability (Cheang
and Choy, 2011; Sundarajan et al., 2002). It includes a range of techniques used
to assess the vulnerability of a financial system to ‘exceptional but plausible’
macroeconomic shocks. It enables us to study the impact of macro-prudential
13
factors on the risk profile of the total financial system (Blaschke, 2001; Sorge,
2004; Cihak, 2007; Oura and Schumacher, 2012).
Micro-stress testing has been used since 1990s. However, Macro-stress testing is
a relatively recent yet an integral concept for measuring financial sector
vulnerabilities (Sorge, 2004). As the traditional micro-prudential regulations were
unable to identify the build-up of systemic risk at the aggregate level, the
analytical focus of research over the last few years has moved from microprudential to macro-prudential dimensions of financial stability (Gadanecz and
Jayaram, 2009; Cheang and Choy, 2011). It enables better monitoring of the
degree of financial stability and anticipates the sources and causes of financial
stress to the system (Gadanecz and Jayaram, 2009). The results of macro
prudential stress tests are often reported in Financial Stability Reports of the
respective countries (Oura and Schumacher, 2012).
There are important differences between the two types of stress tests. The focus
of the macro stress test is, as the name suggests, more macroeconomic in nature
as it attempts to capture the impact of major changes in the macro environment
on the stability of the financial system as a whole. Also, it involves aggregation
of heterogeneous portfolios. Macro stress testing is designed to complement
micro stress testing practices (Jones et al., 2004).
2.1.2. History of Stress Testing
Stress testing began in 1980s to assess the vulnerability of individual institutions on
account of individual risks like credit risks, market risks, interest rate risks, and
14
liquidity risks in isolation. It began to be applied widely by internationally active
banks in 1990s. In 1996, the Basel Committee on Banking Supervision (BCBS)
highlighted the importance of stress testing in its Amendment to the Capital Accord to
Incorporate Market Risks, 1996 (Blaschke, 2001). As per the amendment, ‘banks
should have a rigorous and comprehensive stress testing program in place to identify
events that can greatly influence banks’ capital position. Such Stress Testing should
be of both a quantitative and qualitative nature (BCBS, 1998).
After the Asian Financial crisis of the late 1990s, Financial Sector Assessment
Program (FSAP) was established by IMF in 1999 to provide a comprehensive and
in-depth analysis of a country’s financial sector. FSAP assessments are a joint
responsibility of IMF and World Bank in developing and emerging economies and
IMF alone in advanced economies (IMF, 2016). As part of the FSA Program, IMF
conducts stress tests to examine the resilience of the banking and non-banking
financial sectors. With the advent of assessment of financial stability by IMF and
World Bank, stress testing became an important financial stability assessment tool.
In the year 2000, a task force of G-10 central bank governors was established by
Committee on Global Financial System (CGFS) to discuss issues related to financial
stability. This task force carried out a survey on stress testing in which 43 banks from
10 countries participated. This survey highlighted the risks faced by the financial
institutions and the role of stress tests in risk management (Mosser et al., 2001).
Since the inception of FSAP, 144 member countries (as of 2014) have undergone the
assessment (IMF, 2014). In 2010, the IMF made it mandatory for 25 jurisdictions
(with systematically important financial sectors) to undergo financial stability
15
assessment under FSAP every five years. The list was expanded to 29 jurisdictions in
2013. For all other jurisdictions, FSAP participation continues to be voluntary (IMF,
2014; IMF, 2016).
Traditionally, the focus of prudential data reporting and analysis was on micro
prudential analysis, which was limited to individual institutions (IMF, 2006). The
financial crisis of 2007-08 accelerated the scope and importance of stress testing and
highlighted the limitations of micro-prudential regulations which mainly deal with the
financial and operational conditions of individual financial institutions (Cheang and
Choy, 2011; Kapinos et al., 2015). It underlined the importance of complementing the
micro prudential approach with a macro prudential perspective (Alfaro and
Drehmann, 2009).
Also, with the increasing interdependence of the different
components of the financial system, the growing magnitude and increasing mobility
of international capital flows and the importance of identifying risks that are emerging
in the financial system as a whole, it became important to conduct macro prudential
analysis (IMF, 2006). Hence, stress testing, which was originally developed to be
used at a portfolio level, started being applied in a broader context to measure the
vulnerabilities of a group of financial institutions or the financial system as a whole.
Over the years, the focus of measurement of financial system stability has shifted
from Micro-prudential assessment, which focused around the banking system, to
macro-prudential dimensions of financial stability which incorporates a broader
system-wide assessment of risks pertaining to financial institutions, markets and
infrastructure (Gadanecz and Jayaram, 2009; Cheang and Choy, 2011). Currently, a
lot of work is happening in the area of macro-prudential analysis of financial systems.
16
2.1.3. Importance of Stress Testing
Stress testing is a very useful tool to check the vulnerabilities and robustness of the
financial architecture. As per IMF (2003), Stress testing is a consultative process
between the FSAP and the financial authorities of the respective countries and it
integrates a forward-looking macroeconomic perspective, a focus on the financial
system as a whole, and a uniform approach to the assessment of risk exposures across
institutions. This section puts forward the important reasons why stress testing should
be an integral part of the risk management framework. The following reasons support
the practice of stress testing.
a) Complement Basel norms. The first advantage of stress testing is that it can
complement the Basel norms in capturing systemic risk (Kapinos et al., 2015). Van
Lelyveld (2007) examines how stress testing is an important practice in the context
of Basel II norms and addresses Pillar 1 and Pillar 2 regulations. Wall (2013)
argues that Stress testing could mitigate the weaknesses in the way Basel III
measures credit risk and interest risk and their impact on bank capital. It is more
forward looking and macro prudential in nature as compared to Basel norms which
are more backward looking and micro-prudential in nature (Wall, 2013).
b) Increase transparency in the banking industry. Kapinos et al. (2015) suggest
stress testing leads to dissemination of information which may reduce asymmetric
information in the markets. This may increase the transparency in the banking
industry and be valuable during a financial crisis. Goldstein and Sapra (2012), in
their paper, argue that overall stress testing enables disclosure of unique
information to the investors regarding their risk taking behaviour and capitalisation
thereby promoting market discipline. This further increases their confidence in the
17
banking sector and leads to financial stability; however, there are certain costs that
are associated with this disclosure which can be minimised.
c) Assist in identifying potentially weak banks - Stress tests are designed to
identify the banks that are potentially weak and which require close supervisory
attention and possibly remedial action (Cihak, 2004). It enables regulators and
financial institutions to assess periodically, the possible effects of highly adverse
scenarios on the banks (Kapinos et al., 2015). This enables the regulators to take
appropriate steps in advance to tackle the fragilities which may emerge in the
financial system in case of instability.
d) Support Macro Financial Surveillance - Stress tests play an important role in
macro financial surveillance, i.e., the analysis of the robustness of the financial
system as a whole to external shocks (Cihak, 2004). The most important
responsibility of the central banks is to safeguard financial stability which involves
systematic review of the possible sources of risk to the financial system. Stress
testing models assess these risks and their impact on the financial systems (Henry
and Kok, 2013). It is an important regulatory tool which encourages banks to
engage in more robust and holistic risk management practices (Kapinos et al.,
2015). It also helps the policymakers to assess the significance of the financial
system’s vulnerabilities (Jones et al., 2004).
e) Enhance data availability - Stress testing may enhance the data availability
pertaining to risk management. The information provided by stress tests can help to
identify the weaknesses in data collection, reporting systems, model development,
validation capabilities and risk management (Kapinos et al., 2015; Jones et al.,
2004).
18
2.1.4. VAR and Stress Testing
Stress testing is an important tool that complements the Value at Risk (VaR) analysis.
VaR analysis assigns a single quantitative value to the maximum potential loss that
can result for a portfolio for a given confidence interval over a defined period. It
provides a probability-based boundary on likely losses for a given confidence interval
for a specified holding period (CGFS, 2000). For example, if there is a 90 day VaR on
an asset of USD 100 million at 95 % confidence level, it implies there is a 95%
probability that the maximum possible loss on the portfolio over the next 90 days will
not exceed USD 100 million or in other words, there is only a 5% chance that the
value of the asset will drop more than USD 100 million over the 90 day period. This
5% is captured in the tails of the loss distribution function which are not taken in
account in the VaR analysis. Such extreme losses can be estimated through stress
testing (Kalirai and Scheicher, 2002). The major difference between stress testing and
VaR is that Stress testing measures the risk that arises from abnormal / plausible /
exceptional events whereas VaR analyses the risk from low probability events in the
normal markets. Stress testing methodology, in fact, complements VaR analysis
(CGFS, 2005).
The Committee on Global Financial System (2001) conducted a survey on stress
testing of 43 banks and made an interesting observation. According to the committee,
banks rely heavily on stress tests for markets whose risks may be inadequately
captured by VaR. They conducted interviews of the risk managers who gave several
reasons why they relied more on stress testing rather than VaR. These reasons were:
lack of good historical price data, illiquidity, or difficulties in estimating the highly
non-linear exposures from options dealing (CGFS, 2000).
19
Gerald Krenn (2001) mentions the reasons why stress testing is an important
complementary measure for VaR. The first reason is that a statistical measure like
VaR does not estimate potential extreme losses which Stress testing does. The second
reason he puts forth is that VaR calculations are based on certain assumptions that
may be debatable: for example: VaR models assume that the changes in risk factors
are normally distributed, however, changes in financial time series may be marked by
fat tails, they are not normally distributed. Also, VaR model assumes markets to
remain constant over the given time horizon, however in practice, there may be breaks
in market movements.
2.1.5. Limitations of Stress Testing
Stress testing practices have over the last few years made constant improvements in
terms of design and implementation; however, there are some important limitations of
the stress testing exercise. The following section highlights some of the important
limitations of stress testing:
a) Data Availability - The main impediment of stress testing is the lack of data
availability, especially in countries where the supervisory mechanisms are not well
developed. For example, in some countries, basic balance sheet data is not
available. In some cases, risk data like duration or default measures are not
available (IMF, 2003).
b) Lack of uniformity in methodology aspects – The methodology adopted for
stress testing varies across the countries, which makes a uniform comparison of the
outcomes difficult (IMF, 2003).
20
c) Inability of the existing data reporting systems to isolate the desired exposures
in financial institutions, especially in the case of large and complex financial
institutions (IMF, 2003).
d) Confidentially issues- Sometimes the authorities are unwilling to share the
information with the IMF, which makes the implementation of stress testing
difficult (IMF, 2003).
e) Calibration of scenarios - Stress tests try to examine the impact of shocks that are
severe but plausible. However, such shocks are hardly present over the sample
horizon for which the credit risk model has been developed (Boss et al., 2009).
2.2.
Stress Testing Approaches
There are several important approaches associated with stress testing. The aim of all
these approaches is to examine the potential vulnerabilities of the system from
different perspectives. There is no consensus on what constitutes the best approach to
conducting stress tests. The choice of approach depends upon the objective of stress
testing.
2.2.1. Top Down Vs Bottom Up Approach
There are two types of stress test based on the entity conducting the stress test –
supervisory authorities or the credit institutions (Kearns, 2004). To translate and map
macroeconomic shocks and scenarios into financial sector variables, there can be two
approaches- top-down (TD) or Bottom up (BU).
a) Top-down Approach- Top-down stress testing is conducted to support macroprudential oversight and involves a shock to the macroeconomic environment and
its impact on the financial health of the financial institutions in a centralised
manner (Kearns, 2004; Henry and Kok, 2013). These are the tests which are
21
conducted by the national authorities using bank by bank data and applying
consistent methodology and assumptions (Oura and Schumacher, 2012). In the
Top-down approach, the focus is on the financial stability of the entire financial
system, and is estimated using aggregated data or macro level data (Cihak, 2007;
Moretti et al., 2008). It enables us to estimate the responsiveness of a group of
institutions to a particular scenario (Jones et al., 2004). For example: Bank of
England and Norges Bank employ the top-down approach (Cihak, 2007).
The advantage of Top-down approach is that it is easier to implement and
analyse. Also, this approach gives us consistent and uniform results which may
be easy to compare across countries (Jones et al., 2004). The implementation of
this framework is more resource effective once the core framework is in place as
it employs aggregated data (Oura and Schumacher, 2012). However, as top-down
approach applies tests only to aggregated data, this type of analysis may overlook
the concentration of exposures at the level of individual institutions and linkages
among institutions (Cihak, 2007). Also, due to data limitations, the results may
not be precise (Oura and Schumacher, 2012).
b) Bottom-up approach- Bottom-up exercises are conducted by individual
financial institutions by using their own data and models (Moretti et al., 2008;
Oura and Schumacher, 2012). In the bottom-up approach, the impact to various
scenarios is estimated using highly disaggregated data at portfolio level from
individual financial institutions. These results can further be aggregated for
further analysis (Cihak, 2007; Jones et al., 2004) For e.g., Financial stability
22
reports by Austrian National Bank and Czech National Bank employ the bottomup approach (Cihak, 2007).
The advantage of using bottom-up approach is that it enables better use of
individual, granular, portfolio data. It utilises the internal models developed by
financial institutions which may have been tailor-made as per their own framework
(Jones et al., 2004; Oura and Schumacher, 2012). It also captures the concentration
of risks and the contagion effect and may therefore, give more accurate results
(Cihak, 2007). However, such an approach leads to different interpretations due to
inconsistencies in the application of assumptions and models, calibration of
scenarios etc. This may make the comparison of the results ineffective (Jones et al.,
2004; Oura and Schumacher, 2012). Also, it may also suffer from computational
issues (Cihak, 2007).
The top-down approach can play an important role in benchmarking the results
from system-wide perspective and bottom-up tests can play an important role in
the peer review processes (Henry and Kok, 2013).
2.2.2. Balance Sheet Based Approach Vs Market Price Based Approach
With respect to input information, stress testing models can be broadly divided into
two categories: Balance sheet-based approaches and Market price-based approaches.
a) Balance sheet-based approaches involve a detailed analysis of the balance sheets
of individual institutions (both on - balance sheet and off - balance sheet items).
Such approaches can be more informative as they can enable us to identify the
vulnerabilities in the balance sheet. However, they are highly data intensive and may
23
not capture the contagion effect across institutions (Oura and Schumacher, 2012).
The primary input data for balance sheet based analysis is Accounting data, namely
Balance sheet and Income statement. The frequency of the analysis varies depending
on the reporting cycle which may be quarterly, semi-annually or annually. The
biggest strength of this approach is that it enables the researchers or policymakers to
specify the type of risk that creates vulnerability, for example: losses from currency
mismatches. However, it is highly data intensive; the quality of the analysis depends
on the granularity and the availability of the data (IMF, 2012).
b) Market-based models are based on summary default measures related to market
prices like stocks, bonds etc. These approaches are more flexible and can
incorporate market-perceived risk factors. The primary input data for Market-price
based analysis is the financial market data like equity prices, bond yields etc. This
analysis can be executed at a daily or lower frequency also. This technique is less
data-intensive as compared to the accounting based approach and focuses on
systemic risks and tail events. It incorporates risk factors priced by the market.
However, an important limitation of this approach is that the estimated vulnerability
measures may be very volatile at the times when markets are under stress and
difficult to comprehend (IMF, 2012).
2.3.
Elements of Stress Testing
The following section describes the literature available on various elements of stress
testing. An important element of stress testing is deciding the number of factors to be
included. It can take the form of Sensitivity analysis if one factor is being assessed or
Scenario analysis wherein simultaneously a number of factors are being assessed.
24
A brief description of these two methods is described below:
a) Sensitivity analysis addresses the impact of shocks to single risk factor such as
credit risk or interest rate risk on the financial situation of the bank. Here, only
one factor is subjected to a shock or multiple shocks with all other factors
remaining the same (Blaschke et al., 2001; Marcelo et al., 2008; Moretti et al.;
2008, Wiszniowski, 2010). It involves estimating the change in portfolio value
for one or more shocks to a single risk factor. However, these stress tests do not
allow for the interaction between the macroeconomic variables (Hoggarth et al.,
2005). For example: if the risk factor is exchange rate, sensitivity analysis would
examine the impact of a shock of for e.g., +/-2%, 4% and 6% (CGFS, 2000).
b) Scenario analysis implies the analysis of the effect of simultaneous changes in a
number of risk factors (CGFS, 2000; Blaschke et al., 2001). In this approach,
multiple risk factors are changed together, thereby defining the ‘scenario’
(Marcelo et al., 2008; Moretti et al., 2008). It measures the cumulative effect of
movements in a number of risk factors. It is characterised by a more complicated
structure because of the correlation between individual risks (Wiszniowski,
2010). Scenario analysis can be based on historical data or hypothetical data
(Blaschke et al., 2001):
- Historical scenarios: In the case of historical shocks, shocks are employed
from the past, according to the largest value or change in the variable in a
particular time period. Creation of scenarios using historical data is an
intuitive approach as these events actually happened historically and there
is a high plausibility of them to recur. However, many such scenarios failed
during the financial crisis in 2007-08.
25
- Hypothetical scenarios: In contrast to the historical scenario, in case of
hypothetical data, plausible changes are based on assumptions that have no
historical precedent. They are constructed by shocking market factors,
volatilities or correlations. However, the main drawback in this method is
the difficulty in determining the likelihood of the event occurring as such
an event is beyond the range of experience.
2.4.
Stress Testing Framework
As stress testing practices are still evolving and are based on some assumptions, there
is no uniform framework for Macro stress testing. Different authorities have adopted
different framework for stress testing.
IMF (2003) has summarised the process of stress testing as: (i) identifying potential
risk exposures and vulnerabilities in the system; (ii) identifying the data required and
its availability; (iii) calibrating the scenario or shocks to be applied to the data, based
on identified exceptional but plausible shocks; (iv) selecting and implementing the
methodology; and (v) interpreting the results.
Sorge (2004) describes stress testing process in six steps. The first step is to define the
scope of analysis which includes selection of relevant financial institutions, for
example, large banking institutions, non-banking institutions, insurance companies or
pension funds. The second step is designing and calibrating the macroeconomic stress
scenarios which involves the decisions pertaining to the choice of risk types,
sensitivity or scenario analysis, what parameters to shock, by how much and over
what time horizon. The third step is assessing the system vulnerability to specific risk
26
factors which may include the choice of indicators, namely the Financial Soundness
indicators (FSIs) that quantify the systemic importance of various sources of risks.
Sorge further suggests integrating the analysis of the market and credit risks. The fifth
step involves aggregation of the results and their interpretation. The final step
involves the analysis of the contagion effects, also called the feedback effects.
Jones et al. (2004), in an IMF working paper, have also discussed the key stages of
stress testing. The first step is the identification of vulnerabilities which could be
classified as macro-level indicators (ex. Real sector indicators, external sector
indicators), structural indicators (ex. Balance sheet structures, flow of funds accounts)
and financial soundness indicators (capital adequacy, asset quality, liquidity etc.). The
next step involves examination of the available data and models and construction of a
scenario in the context of the overall macroeconomic framework. The third step is to
translate the various outputs that have been derived into the balance sheets and
income statements of the financial institutions and the calibration of shocks based on
hypothetical or historical data. The next step is to consider the second round effects
and linkages between the financial institutions which may further be used to construct
indices of systemic risk. The final step is the interpretation of the results.
Cihak (2007) has also has explained the stress testing process on similar lines. He
describes stress testing as a process that includes a) identification of concern areas or
specific vulnerabilities; b) construction of a scenario; c) mapping the outputs of the
scenario into a usable form; d) performing the numerical analysis; e) examination of
second round effects; and finally, f) summarizing and interpreting the results.
27
Henry and Kok (2013) have explained stress testing as a modular system consisting of
four pillars. The first pillar is Scenario Design which consists of the design of the
macro-financial scenarios to be imposed on the banking sector. The second pillar is
Top-Down Satellite Models which consists of the models that are used to translate the
scenarios into variables that affect the balance sheet components. The third pillar is
the Balance Sheet Model that calculates the impact on the bank’s solvency position.
The fourth pillar is Feedback Module. Normally, the Macro Stress testing exercises
examine only the “first-round” impact of the stressed banks’ solvency position on
bank capitalisation. However the banks react to stressed situations by adjusting their
Balance Sheets in some ways which impact the other banks in the system and thereby
have important ramifications on the real economy. This is called contagion effect. The
fourth pillar facilitates examination of the second round effects of the initial bank
solvency impact with respect to contagion effect by linking the results of the stress
testing framework to the broader macro economy.
2.5.
Risks Covered Under Stress Test
Basel Committee on Banking Supervision (BCBS, 2005) and RBI Master Circular
(2013) classify banking risks into three major categories - Credit Risk, Market Risk,
and Operational Risk.
- Credit risk or default risk is defined as the potential that a bank borrower or
counterparty will fail to meet its obligations in accordance with agreed terms.
-
Market Risk is defined as the risk of losses in ‘on-balance sheet’ and ‘off-balance
sheet’ positions arising from movements in market prices.
-
Operational risk is defined as the risk of loss resulting from inadequate or failed
internal processes, people and systems or from external events.
28
Apart from the above three major types of bank risks, the Basel Committee also
identified Liquidity Risk, Interest Rate Risk, and ‘Other’ Risks (i.e. reputational and
strategic risk). Several other types of risks have also been identified in different
studies. Raghavan (2003) suggested that bank risk comprises of Credit Risk, Market
Risk (comprising of liquidity risk, interest rate risk, forex risk, and country risk),
Operational Risk, Regulatory Risk and Environmental Risk. FSAP addresses the
following risks as part of stress testing (Blaschke et al., 2001), both micro stress
testing and macro stress testing.
- Interest rate risk: Interest rate risk is the risk incurred by a financial institution in
case of mismatch of rate sensitive assets and rate sensitive liabilities.
- Exchange rate risk: Exchange rate risk is the risk arising out of the changes in the
exchange rates which affects the value of institution’s assets and liabilities as well
as any off-balance sheet items.
- Credit risk: Credit risk is the risk that a counter-party or obligor will default on
their contractual obligations.
- Liquidity risk: Liquidity risk is the risk arising from a financial institutions
inability to meet its obligations as and when they become due.
- Equity price risk: Equity price risk is the risk that the stock price changes affect
the value of a financial institutions assets and liabilities and its off-balance sheet
items.
- Commodity Price Risk: Commodity price risk refers to the potential losses that
may result from changes in the market price of bank assets and liabilities as well as
off-balance sheet instruments due to commodity price changes.
29
- Market Risk: Market risk is the risk of losses on a portfolio arising from
movements in market prices.
2.6.
Stress Testing and Basel
In January 1996, Basel Committee on Banking Supervision in its ‘Amendment to the
Capital Accord to Incorporate Market Risk’ advocated that the banks that use Internal
Models approach for meeting market risk capital requirements must have a rigorous
and comprehensive stress testing program as a supplement to the risk analysis. It
further proposed that such stress tests must be both quantitative and qualitative in
nature. Quantitative criteria should identify the plausible scenarios that the banks are
exposed to and qualitative criteria should emphasise on the evaluation of bank’s
capital adequacy and steps to reduce the risk. It should cover a range of factors which
identify the plausible stress scenarios banks are exposed to and examine the capacity
of banks to absorb such potential losses and manage the risks (BCBS, 1998).
The June 2006 revised Basel Framework titled ‘International Convergence of Capital
Measurement and Capital Standards’ also reiterates the importance of a rigorous,
forward looking stress testing framework that identifies possible events or changes in
market conditions that could affect bank performance adversely and for the
assessment of capital adequacy. As per the guidelines “Stress testing must involve
identifying possible events or future changes in economic conditions that could have
unfavourable effects on a firm’s credit exposures and assessment of the firm’s ability
to withstand such changes. Examples of scenarios that could be used are; (i)
economic or industry downturns, (ii) market-place events, or (iii) decreased liquidity
conditions.” Also, banks must ensure sufficient capital to meet the minimum capital
requirements to cover the results of the stress testing programme (BCBS, 2006).
30
Larry D Wall (2103) in his paper shows how stress testing could mitigate the
weaknesses in the way Basel III measures credit and interest rate risk and measures
bank capital. Stress testing adds value to Basel III guidelines by providing more
flexibility in the implementation of the stress tests and measuring the losses associated
with a handful of specific scenarios which are not covered by Basel III. Basel III
provides an unconditional static measure for calculating capital adequacy whereas
stress testing applies conditional dynamic measures for the same (Wall, 2013).
2.7.
Stress Testing Practices Based On Geography
Stress testing practices vary across the globe. Some countries have advanced stress
testing techniques in place whereas in the case of some countries, the research is in an
evolving stage. BCBS conducted a survey on the ‘Peer review of supervisory
authorities’ implementation of stress testing principles’ in April 2012 in which all the
member countries participated (BCBS, 2012). As per the report, in 50% of the
respondent countries, the practice of stress testing was in its ‘early stages’ wherein
these countries may have showed some progress towards implementing the principles
but may not have finalised the prudential regulations. A major part of the other half of
the respondents were in the ‘intermediate category’ wherein these countries have
issued some formal guidelines or guidance consistent with the principles and
performing regular supervisory tests but they may require more detailed stress testing
mechanisms. Only a few countries were in the advanced stage which had evidence of
rigorous and regular review process. The report was not designed to assess the
adequacy of the banks’ stress testing programmes; therefore country wise analysis
was not provided (BCBS, 2012).
31
This section discusses the literature available in the area of stress testing based on
geography.
- United States of America (US) - In US, there was a Supervisory Capital
Assessment program (SCAP) in 1999 which was an assessment of the financial
conditions of the largest Bank Holding Companies (BHCs). Currently in US, two
sets of stress tests are conducted annually by Federal Reserve to ensure that the
financial systems have adequate capital planning process. These two tests are a)
Comprehensive Capital Analysis and Review (CCAR) and b) Dodd-Frank Act
(DFA) supervisory stress testing. CCAR, similar in scope to micro stress testing,
is a regulatory framework to assess, regulate and supervise large banks and
financial institutions in terms of capital adequacy requirements of bank holding
companies (BHCs) in the US. DFA, closer in scope to macro stress testing, is a
forward looking quantitative evaluation of stressful economic and financial market
conditions on capital of BHCs in US. DFA aimed at improving the stability of the
US financial system (Federal Reserve, 2013; Henry and Kok, 2013).
- European Union – In Europe, the European Systematic Risk Board (ESRB) was
established in 2010 to carry out macro-prudential oversight of the financial system
and to prevent / mitigate systematic risks. The work of ESRB is complemented by
3 European Supervisory Authorities (ESA) which consist of European Banking
Authority
(EBA)
in
London,
The European
Securities
and
Markets
Authority (ESMA) in Paris, and The European Insurance and Occupational
Pensions Authority (EIOPA) in Frankfurt. The European Union banking stress
tests are conducted by EBA to check the resilience of the financial situations
32
towards adverse scenarios (Henry and Kok, 2013). EBA conducts stress tests
using bottom-up approach using consistent methodologies, scenarios and key
assumptions in cooperation with ESRB, The European Central Bank (ECB) and
the European Commission (EC) (EBA, 2016).
-
Australia – Australian Prudential Regulation Authority (APRA) is the prudential
regulator of the Australian financial services industry that focuses on the stress
testing practices in Australia. It conducts industry-wise stress testing once in 2-3
years in contrast to other countries wherein stress testing is an annual practice. It is
primarily a capital adequacy assessment with a review of banks’ models and
assumptions (Oliver Wyman, 2014). The annual FSAP assessment update for
Australia found the Australia’s financial system sound, resilient and well managed
(FSAP - IMF, 2012).
- Asia - The impact of the global financial crisis was not as much on Asian countries
as compared to US and Europe. Therefore, these nations have not adopted robust
stress testing mechanisms as compared to the western nations. Nevertheless, these
countries have adopted stress testing mechanisms as part of their regular financial
stability assessments (Wyman, 2015). In a report by Oliver Wyman on Asia Stress
Test (2015), they have categorised the Asian stress testing practices with respect to
the US and European practices in four categories: Evolving (India, China and
Indonesia); Developing (South Korea, Japan, Hong Kong, Malaysia and
Singapore); Developed (European Union and Australia) and Advanced (United
States).
33
As the focus of our thesis is on the evaluation of stress testing practices in the Indian
context, the below section examines in detail the literature available in this area.
2.7.1. Stress Testing – Indian Practice
In the Indian context, banks are required to operationalize their formal stress testing
framework in accordance with the guidelines issued by Reserve Bank of India from
March 2008 (RBI – Guidelines on Stress testing circular dated June 26, 2007). After
the global financial crisis, there was a paradigm shift in the approach of policymakers
towards financial stability. The depth of the crisis made the supervisory authorities to
assess the robustness of these tests as the crisis was far more severe than the many
assumptions that had been taken for the existing stress testing practices (RBI –
Guidelines on Stress testing, 2013). Keeping in view the changing paradigms, RBI
established a Financial Stability Unit in August 2009. To improve the transparency of
the financial system, it was decide to publish a periodic ‘Financial Stability Report’
(FSR). It is a bi-annual document that reviews the nature, magnitude and implications
of risks and their impact on the macroeconomic environment and eventually financial
institutions. The first FSR was published in March 2010 and till date (as on dec 31,
2018) 18 issues have been published. The first FSR dealt only with single factor
sensitivity analysis at individual risk level as macro stress testing was still at an
evolving stage. Macro stress testing was introduced from the second FSR Dec 2010.
a)
Guidelines for Stress Testing - RBI
RBI issued regulatory guidelines and guidance notes on asset liability management
and management of credit risk, market risk and operational risk in 1999 (RBI
Notification
date
June
26,
2007
on
Guidelines
on
Stress
testing.
https://rbidocs.rbi.org.in/rdocs/notification/PDFs/78232.pdf). Taking this momentum
34
forward and in line with pillar 2 of Basel II framework, the draft guidelines for stress
testing were issued in 2007. Thereafter, banks were advised to put in place
appropriate stress testing policies and framework by September 2007 and ensure that
a formal stress testing framework was operational from March 31, 2008. The
guidelines lay down the two categories of stress tests to be developed by banks:
a) Sensitivity tests as the tests that assess the impact of change in one variable; and
b) Scenario tests as a simultaneous movement in a number of variables based on
either historical scenario or hypothetical scenarios.
The report further highlights the importance of stress tests and lays down the
framework requirements for stress testing. It also identifies the risks that should be
subjected to stress tests as market risks, credit risks, operational risks and liquidity
funding risks. The guidelines mention the risk categories along with the frequency of
the stress testing and effective date for stress tests. Finally, it provides the illustrative
examples of stress tests for Liquidity risk, Interest rate risk –earnings perspective,
Credit risk-impact on capital adequacy, Credit risk-impact of increasing NPAs and
Foreign exchange risk (RBI Notification date June 26, 2007 on Guidelines on Stress
testing. https://rbidocs.rbi.org.in/rdocs/notification/PDFs/78232.pdf).
In Dec 2013, RBI issued updated guidelines on stress testing in light of the revised
guidelines issues by Basel Committee on Banking Supervision (BCBS) on Sound
Stress Testing Practices and Supervision. The banks were expected to adopt these
guidelines on stress testing from April 1, 2014. The need of the revised guidelines was
felt after the 2007-08 global financial crises which brought into focus the limitations
of the risks assessed through stress testing based on mainly historical data and
35
assumptions. Thereafter, a need was felt to make the stress testing program more
rigorous and stringent and raise the level of sophistication of such programmes (RBI
Notification
date
December
2,
2013
on
Guidelines
on
Stress
testing.
https://rbidocs.rbi.org.in/rdocs/notification/PDFs/FC021212ST.pdf).
b) Stress testing Assessment from 2010-2018- Financial Stability Reports (FSR)
The resilience of the scheduled commercial banks is analysed under two broad
categories i.e. a) Bank’s performance and b) Stress test. It measures the resilience of
the Indian banking system by performing macro stress tests for credit risk at the a)
System level b) Bank group level and c) Sectoral level. FSRs review the health of the
financial system and focuses on issues pertaining to systemic importance. Based on
10 years historical data, 3 macro-economic scenarios are analysed which include one
baseline scenario and 2 adverse macroeconomic risks namely Medium risk –based on
upto 1 standard deviation (10 yrs historical data) and Severe risk - based on upto 2
standard deviations. The FSR employs the following Time Series Econometric
models: a) Multivariate Regression to model system Level Slippage Ratio (SR)
b) Vector Autoregression (VAR) to model system level SR; c) Quantile Regression to
model system level SR; d) Multivariate Regression to model bank group-wise SR; e)
VAR To model bank group-wise SR; and f) Multivariate Regressions for Sectoral
GNPAs.
c)
Financial Sector Assessment Programme (FSAP) India Update
The third set of documents reviewed was related to the Financial Sector Assessments
Programme updates conducted in the Indian context. As mentioned earlier, FSAP is a
joint program of the IMF and World Bank which performs a comprehensive and in
depth analysis of a country’s financial sector. In 2000-2001, India’s FSAP was
36
conducted as a pilot assessment; however the results were not made public. In
September 2010, IMF made it mandatory for 25 jurisdictions (including India) to
undergo financial stability assessments under FSAP every five years. As part of this
program, IMF conducted India’s FSAP during 2011, the results of which were
published on January 15, 2013.
The main findings of FSAP was that stress testing did not reveal any stability
concerns in the near term suggesting further that the banking system in India would be
resilient to a range of adverse shocks. However, it also reported that the financial
system is becoming more complex and with increasing inter-linkages across borders
and institutions, the systemic risks have increased which have highlighted the
challenges and vulnerabilities in the financial system (IMF – India: Financial Stability
Assessment Update, 2013).
d) Research Papers
The research on Macro stress testing in the Indian context is still in its nascent stage
which may be attributed to lack of availability of good quality data and complex
econometric techniques. There are only a few papers available in this area. However,
a lot of papers in the area of Micro stress testing and credit risk determinants in the
Indian scenario can be found.
Roy and Bhattacharya (2011) have examined the resilience of the Indian Banking
sector through Macroeconomic Stress Testing for credit risk using a VAR
methodology. The authors examine the dynamic impact of changes in macroeconomic
variables like output gap, real effective exchange rate, inflation, bank rate, repo rate
and reverse repo rate on Default rate with respect to Indian Public Sector Banks.
37
However, the choice of determinants in the paper is very limited (Roy and
Bhattacharya, 2011). Banerjee and Murali (2015) also employ VAR approach for
conducting stress tests for the Indian Banking sector. The authors verify the results
through Granger Causality, Impulse Response Function (IRF) and Forecast Error
Variance Decomposition (FEVD). In this paper, NPAs are regressed in a VAR model
on log of nominal exchange rate, Net FII, GDP output gap (Actual GDP-potential
GDP), log of deposits, log of nominal interest prime lending rate, CRR and WPI. It
proposes re-capitalisation of all banks and improvement of asset quality (Banerjee and
Murali, 2015). Das and Ghosh (2007) investigate empirically the determinants of
credit risk in Indian Public Sector banks using advanced Panel data techniques. The
findings of the paper are that at the macro level, GDP growth plays an important role
in influencing the default loans and at the level of the banks, real loan growth and
bank size are important determinants of nonperforming loans (Das and Ghosh, 2007).
Thiagarajan et al. (2011) also perform an empirical investigation of the credit risk
determinants in the Indian context through a Panel data technique. However, the
authors conduct this analysis for both Private and Public banks. The study reveals that
both the macroeconomic and bank specific factors play a very important role in
determining the credit risk in the bbanking sector. As per the study, GDP growth and
lagged NPA are the main determinants of NPA (Thiagarajan et al., 2011).
2.8.
Methodology Wise Literature Review
There are a lot of approaches, models and scenarios available to conduct macro stress
testing. Also, depending on the economic environment and the legal norms prevalent
in a particular country, the underlying assumptions for stress tests are different which
makes the cross-country comparisons of the stress testing implications difficult.
Hence, there cannot be ‘one approach fits all’ in case of stress testing. However,
38
researchers have made an attempt to classify the methodology on some basis. This
section presents the literature review on the methodologies adopted for conducting
macro stress tests. Consolidating the literature based on methodology, there are two
broad classifications followed by researchers while explaining the modelling
approaches of macro stress testing: These two classifications are: Classification by
Marco Sorge (2004) and Classification by Antonella Foglia (2009). Figure 2.1 shows
the methodology based classification.
2.8 Macro Stress Testing
Methodology - Classification
2.8.2 Classification
by Foglia, 2009
2.8.1Classification by
Sorge, 2004
2.8.1.1 Piecewise
Approach
2.8.1.2 Integrated
Approach
2.8.2.1 Structural
econometric models
2.8.2.2 Vectorautoregressive
models
2.8.1.1 a) Reduced
form relationship
model
2.8.1.1 b) Structural models
2.8.2.3Statistical
approaches
Time Series analysis
2.8.2.4 Judgemental approach
(added by Melecky and Podpiera,
2010)
Panel Regression analysis
Source: compiled from research papers
Figure 1.1: Macro Stress Testing – Classification Based On Methodology
39
2.8.1. Classification by Sorge, 2004
In the first category of classification, Sorge (2004) categorised stress testing methods
in two broad categories. This classification has been followed by various researchers
thereafter.
-
Piecewise Approach and
-
Integrated Approach
2.8.1.1.
Piecewise Approach
Piecewise approach involves estimating causal relationship of a macro shock on
financial variables individually. It evaluates the vulnerability of the financial sector to
single risk factors, by forecasting several “financial soundness indicators” (such as
nonperforming advances, capital ratios, etc.) under various macroeconomic stress
scenarios (Sorge, 2004; Marcelo et al., 2008; India - Financial Stability Report,
December 2010). It estimates the impact of a macroeconomic shock on a single
financial soundness indicator.
The basic analytical framework of this approach includes estimation of a direct
relationship between macro fundamentals (X) and risk measures i.e. FSIs (Y) through
an econometric model based on historical data. Once the estimated coefficients have
been derived from the econometric model, these are used to simulate the impact of
adverse macro scenarios on the vulnerability of the financial system (Sorge, 2004).
This approach can be represented as
(
,
≥
= {
40
,
}
where, for each portfolio i and time t, Y is the measure of default. For example, nonperforming loan ratio or loan loss reserve which is estimated as a linear function of
past realisations of a vector X of relevant macro variables (GDP, Inflation, interest
rates, stock market indices, unemployment etc.). It can also include vector Z of
exogenous bank specific variables like bank size, capitalisation etc. An important
point to note is that macro stress testing is forecasting Y under extreme assumptions
for macroeconomic variables as denoted by tail realisation of
≥
(Sorge,
2004). Generally, these models are relatively simple to implement. However, an
important limitation of this approach relates to the rigid linear relationships that are
usually estimated between macro variables and bank risks. These models further can
be classified into two subsets.
-
Reduced form relationship models: models that estimate the equation as
reduced form relationship using either time-series or panel data techniques.
-
Structural models: models that analyse the fragility of the banking system
due to changes in macroeconomic indicators in the economy wise or interindustry structural models.
Several papers have adopted these two modelling options while conducting macro
stress testing. The below section reviews some of papers based on the above
categorisation.
2.8.1.1.1
Reduced Form Relationship Models
Reduced form relationship models are the models that specify a particular relationship
between a balance sheet indicator (in our case a financial soundness indicator) and
41
macroeconomic variables by a single equation. These models can be conducted as
time series analysis or panel data analysis. The two forms have been discussed below:
- Time Series Analysis: One of the simplest techniques to implement stress testing is
Time-series analysis. In this technique, regression equations are employed using
historical data to establish the relationship between measures of default and
macroeconomic variables. Thereafter, the researchers can assess the vulnerability
of the financial system by bringing about shocks to the macroeconomic variables
(Sorge, 2004; Howard, 2008). The below section discusses literature on time
series analysis:
Kalirai and Scheicher (2002), perform macro stress testing for the Austrian banking
system from 1990 to 2001 using time series regression with respect to Loan Loss
Provision (LLP) as a proxy for credit risk and a host of the macro economic factors.
These macroeconomic factors have been categorised as Cyclical Indicators (GDP,
Industrial Production and Output gap), Price Stability Indicators (Inflation and Money
Growth), Household Indicators (Consumption Expenditure, Unemployment Rate,
Employee
Compensation
and
New-Car
Registration),
Corporate
Indicators
(Investment Expenditure, Gross Fixed Capital Formation, Productivity per Employee,
Business Confidence and Bankruptcies), financial Market Indicators (Interest Rates,
Stock Indices) and External Variables (Exports, Exchange Rates and Oil Prices). The
authors have taken a comprehensive set of variables for conducting macro stress tests.
Hoggarth and Zicchino (2005) employ the Vector Autoregressive (VAR) approach
to stress testing the UK banking system. Their work takes into account the feedback
42
effects of the fragility of banks’ balance sheet to the macro economy. The paper
focuses on the relationship between banks’ write offs as reflected by ‘the write-off to
loan ratio’ (household and corporate write-offs both aggregate and disintegrated) and
macroeconomic variables like UK output gap, nominal interest rate, inflation and real
exchange rate. This paper is an important paper in terms of methodology adopted. The
authors employed a new approach to stress testing and paved way for many further
studies.
Hanschel and Monnin (2005) have developed a ‘composite stress index’ which
summarises the condition of the Swiss banking sector in a single measure and enables
the forecast of the stress index using macroeconomic variables. They used four types
of variables to build the stress index namely, market price data, balance sheet data,
non-public data and other structural variables.
Roy and Bhattacharya (2011) have also employed Recursive Vector Autoregression
(RVAR) model to investigate the dynamic impact of changes in the macroeconomic
variables on the default rate. The default rate employed in the study is based on gross
NPAs and gross advances, while the macroeconomic variables are Output Gap,
Consumer Price Index, Real Effective Exchange rate, Bank rate, repo rate and reverse
repo rate. This paper is one of the few studies on macro stress testing conducted in the
Indian context.
Vazquez et al. (2012) use time series econometrics to establish a relationship
between selected macroeconomic variables like Credit growth, GDP growth and
43
changes in the yield curve and use the results to build a stressed scenario in the
context of the Brazilian economy.
Reserve Bank of India in its bi annual test of resilience of Indian banking system
(Financial Stability Reports) also uses time series analysis to model credit risk as a
function of macroeconomic variables. The macroeconomic variables employed for the
study are Gross value added at basic price, weighted average lending rate, CPI
(combined inflation), exports to GDP ratio, current account balance to GDP ratio and
gross fiscal deficit to GDP ratio. Multivariate Regression, Vector Autoregression
(VAR) and Quantile Regression models have been adopted for econometric analysis.
The advantage of time series analysis is that it is one of the simplest techniques to
apply, but at the same time, the technique suffers from an important limitation in that
it aggregates the microeconomic defaults that lead to financial stress (Howard, 2008).
- Panel Data Regression: Another segment of reduced form relationship is Panel
data regression. Some researchers have conducted macroeconomic stress testing
using panel data regression in two forms: panel data for aggregate banking
system across the countries or individual banks within a single country.
The studies based on panel data across countries for aggregate banking include a
study by Pesola (2001) who has employed an econometric model using panel data for
analysing the macroeconomic reasons for the banking crisis in the four Nordic
Countries (Denmark, Norway, Sweden and Finland) during the 1980s and 1990s. The
macroeconomic variables used were lagged dependent variables, lagged percentage
44
change in GDP, an income variable combined with lagged aggregate indebtedness, a
real interest rate variable combined with lagged aggregate indebtedness and a
deregulation dummy. The author used two alternative dependent variables: a) ratio of
banks’ loan losses to lending (loan losses per banks’ outstanding lending stock) and b)
enterprise bankruptcies per capita.
Bikker and Hu (2002) used panel data regression to examine the relationship
between macroeconomic variables and provisions for credit losses for 26 industrial
countries. The macroeconomic variables used by the author are GDP, inflation, loans,
net profits and failures.
The second section where panel study is conducted across individual banks within a
single country includes studies by Yap (2011) who has employed panel data
regression for 29 domestic Indonesian banks to develop a credit model to detect the
susceptibility of banking sector to get into financial distress by establishing the
relationship between macroeconomic determinants and loan loss provisions.
Clair (2004) provides a study of possible macroeconomic determinants of changes in
Singapore’s financial performance and resilience. Initially, he identifies two sets of
factors, namely macroeconomic factors and bank nominal data. Out of 30 indicators
chosen for analysis, only around 12 factors contained important information. As per
the author, an ideal methodology would be to build a multi-equation framework that
captures inter linkages between income statement and balance sheet by following a
structural simultaneous equation system. However, due high volatility of bank data, a
reduced form Vector Auto regression (VAR) model was derived. Thereafter, Panel
45
regression analysis was utilised to explore the effect of the macroeconomic cycle on
banks while testing for interbank differences. Further stress testing was carried out.
Kosmidou and Moutsianas (2015) have developed a macro stress testing framework
to assess the stability of Greek banking system using Panel data regression using GDP
growth, inflation, Industrial production index, unemployment rate and economic
sentimental indicator as the macroeconomic variables.
Gerlach et al. (2005) have used the panel data approach for 29 retail banks in
Hongkong SAR to study the impact of macroeconomic developments on bank
profitability (as reflected by Net interest margin) and solvency (as reflected by NPL
ratio). The authors have used 3 sets of variables in their study - macroeconomic
variables like economic growth and inflation to examine the state of the economy,
financial variables such as interest rates and changes in property prices and bank
specific variables such as asset size and sectoral concentration in lending.
Vazquez et al. (2012), in their paper, use panel data econometrics to perform macro
stress testing of credit risk based on scenario analysis as one of the objectives. They
estimate the sensitivity of NPLs to macro variables like GDP growth, credit growth
and interest rates.
Hadad et al. (2007) also use Panel data regression (General Unrestricted Panel
Model) for the Indonesian banking system using the data of 131 banks. The variables
employed in the study are GDP, Inflation, money growth, Sertifikat Bank of
Indonesia’s rate, foreign exchange, gasoline prices and diesel fuel prices.
46
2.8.1.1.2
Structural Models
Structural models are usually employed by the central banks for forecast and policy
analysis (Sorge, 2004; Howard, 2008). Foglia (2008) states that in structural models,
‘a set of initial shocks are taken as exogenous inputs and their interactions with the
other macroeconomic variables are projected over the scenario horizon. The
simulations will produce a range of economic and financial variables as outputs, such
as GDP, interest rates, the exchange rate, and other variables’. The advantage of
such structural models is that their use leads to consistency across the predicted values
in the stress scenario. They also allow for endogenous policy reactions to the initial
shock.
2.8.1.2. Integrated Approach
An integrated approach involves combining the analysis of multiple risk factors into
a single estimate of the probability distribution of aggregate losses that could
materialise in a given stress scenario. It combines the analysis of the sensitivity of the
financial system to multiple risk factors into a single estimate of the probability
distribution of aggregate losses that could materialise under any given stress scenario
(Sorge, 2004; Marcelo et al., 2008; India-Financial Stability Report, December 2010).
This section reviews the literature available related to integrated risk models that take
into account multiple sources of risk together and provide an integrated assessment of
the overall vulnerability of the financial systems.
One of the initial developments in the integrated modelling was Systematic Risk
Model (SRM) that has been developed by the Austrian Central Bank
47
(Oesterreichische Nationalbank – OeNB) for stress testing and financial stability
analysis. It is an integrated framework that combines credit risk, market risk and
interbank contagion risk (Boss et al., 2006). Another integrated model, the Risk
Assessment Model of Systematic Institutions (RAMSI) model that has been
developed by the Bank of England, integrates credit, market and liquidity risks.
RAMSI is an example of a top-down stress testing model. In this model, a macro
shock leads to both market and credit losses and leads to downgrading of bank’s
liquidity through a scoring system (Demekas, 2015; Burrows et al., 2012; Aikman et
al., 2009).
Wong and Hui (2009) developed a liquidity stress testing framework with interaction
between market and credit risks. This paper highlights an integral aspect wherein
liquidity risk and default risks of financial institutions are inter-related (Wong and
Hui, 2009). Barnhill and Schumacher (2011) proposed an integrated methodology
for modelling correlated systematic solvency and liquidity risks for the banking
system to estimate the probability that multiple banks will fail or face liquidity runs
simultaneously. In 2012, Macro Financial Risk Assessment Framework (MFRAF)
was developed by Bank of Canada. This model goes beyond most macro-stress testing
methods and integrates funding liquidity risk, solvency risk and the spill over effects
of the interbank exposures (Gautheir and Souissi, 2012).
48
The below table summarises the important differences between Piecewise approach
and Integrated Approach to Macro stress testing.
Table No 2.1: Piecewise vs Integrated Approach
“PIECEWISE APPROACH”
Forecasting models of individual
financial soundness indicators
MAIN
MODELLING
OPTIONS
Time series or Panel data
“INTEGRATED APPROACH”
Combining the analysis of
multiple risk factors into a single
portfolio loss distribution
Macro-econometric risk model á
la Wilson (1997)
Reduced-form or Structural
Micro-structural risk model á la
models
Merton (1974)
Intuitive and with low
Integrates analysis of market and
computational burden
credit risks
Simulates shift in entire loss
PROS
Broader characterisation of stress
distribution driven by the impact
scenario
of macroeconomic shocks on
individual risk components’
Has been applied to capture
Monetary policy trade-offs
nonlinear effects of macro shocks
on credit risk
CONS
Mostly linear functional forms
Non-additivity of value-at-risk
have been used
measures across institutions
Parameter instability over longer
horizons
Most models so far have focused
on credit risk only, usually limited
to short-term horizon
Available studies have not dealt
No feedback effects
with feedback effects
Loan loss provisions and nonperforming loans may be noisy
indicators of credit risk
Source: Sorge 2004
49
2.8.2. Classification by Foglia, 2009
The second set of classification was introduced by Foglia (2009) and thereafter has
been followed by many researchers. As per this classification there are 3 types of
methodologies.
a) Structural Econometric models
b) Vector-Autoregressive models
c) Pure Statistical approaches.
Melecky and Podpiera, (2010) added another approach to the above 3 approaches.
d) Judgemental approach.
2.8.2.1 Structural Econometric Models: same as explained in section 2.8.1.1. b)
Structural models.
2.8.2.2 Vector-Autoregressive Models: Sometimes a well developed structural
macroeconomic model is not available, in which case Vector Autoregression (VAR)
or Vector Error Correction Model (VECM) model can be used. (Melecky and
Podpiera, 2010). ‘In these models, a set of macroeconomic variables are jointly
affected by the initial shock, and the vector process is used to project the stress
scenario’s combined impact on this set of variables’ (Foglia, 2009). Foglia (2009)
asserted that VAR models are flexible and relatively simple to employ and interpret,
however these models do not incorporate the economic structure as in macro
modelling approach. VAR model are in fact part of time series models as discussed in
the section-reduced form of equations. VAR models as discussed in Time Series
analysis can be referred to for further study.
50
2.8.2.3 Pure Statistical Approaches: In the pure statistical approaches, the
macroeconomic and financial variables are modelled through a multivariate t-copula.
This enables researchers to work on marginal distributions instead of multivariate
distributions and allows capturing the co-dependence among macro-financial
variables at the time of stress by recording of higher moments’ dependence among
macro- financial variables. However, such an approach may not be suitable for policy
analysis (Foglia, 2009; Melecky and Podpiera, 2010). For example- Systematic Risk
Monitor (SRM) model as developed by Oesterreichische Nationalbank (OeNB) (Boss
et al., 2006).
2.8.2.4 Judgemental Approach: Melecky and Podpiera (2010) argue that sometimes
statistical or econometric models may not be capable of producing appropriate stress
scenarios due to lack of historical data; in such a case judgemental approach is used.
Judgemental models allow more robust cross-country analysis, particularly by
employing the experience of the countries that suffered financial crisis in deriving the
shocks to the macroeconomic variables. However, such an approach may not be
considered consistent with the structural macroeconomic models (Melecky and
Podpiera, 2010).
2.9
Credit Risk Modelling
As described above, credit risk implies to the possibility of credit losses due to nonrepayment or failure of the borrower to meet contractual loan obligations. It is one of
the most dominant risk categories pertaining to the banking sector and impacts the
capital adequacy requirements the most. Therefore, from the perspective of macro
stress testing, Credit Risk Modelling is one of the most important aspects of the
overall analytical risk framework. The objective of Credit Risk Modelling is to
51
establish relationship between the credit risk parameters. This area has been widely
analysed. There are two important approaches which enable us to evaluate and
analyse the relationship between Credit risk and Macroeconomic variables
a)
Merton model – One of the early models of credit risk was developed by Merton
(1974). In his initial work, he assumed that value of assets of a firm follow a
stochastic process (Quagliariello, 2009). According to this model, a firm is
expected to default when the value of its assets falls below a threshold value of its
liabilities (Pesaran et al., 2006; Drehmann, 2005). This model consists of
modelling the response of equity prices to macroeconomic factors after which the
asset price movements are mapped into default probabilities (Avouyi et al.,
2009). Several researchers have employed the Merton model for stress testing.
Drehmann (2005) applied the Merton model for corporate exposures of UK
banks. Another important paper in this area is written by Pesaran et al. (2005)
who developed the relationship between macroeconomic dynamics and credit risk
from a global perspective (Pesaran et al., 2006). However, this model had an
important limitation. It relied on market prices which were not available for the
non-listed companies and households, which constituted a large part of banks’
portfolios. The model also suffered from several empirical problems like
robustness (Quagliariello, 2009).
b) Wilson Model – The other important model for credit risk modelling was
developed by Wilson (1997a and b). In case of the Wilson model, the default rate
of an economic sector is directly related to macroeconomic factors. Wilson
employed probit model and identified macroeconomic factors as systemic risk
52
drivers along with firm-specific factors (Quagliariello, 2009). This model
thereafter has been employed by several authors for further research. This model
was applied by Boss (2002) in case of Austria and Virolainen (2004) in case of
Finland and Choi Fong Wong (2008) in case of Hong Kong.
Table 2.2 provides the summary of literature review
Table 2.2 Summary of Literature Review
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
Gross
Financial
NPA/
Stability Reports
India
March 2010 to
Dec 2016
2010-
Total
2016
advances
(in form of
Growth Indicators -Real GDP, Real
GDP-agriculture,
Real
GDP-
industry, Industrial production
logit npa)
Price stability indicators- WPI, CPI,
M3
Interest Rate (Nominal) indicators Interest rate short term, interest rate
long term
Financial market indicators- Sensex,
Nifty
External sector indicators - Exchange
rate (USD), REER, Exports, Trade
53
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
log of nominal exchange rate, Net
Banerjee
and
Murali (2015)
India
19972012
FII, GDP output gap (Actual GDPNPA
potential GDP), log of deposits, log
of nominal interest prime lending
rate, CRR, WPI
NPL/(1-NPL) Non-performing loans,
GDPGR Growth rate of real GDP,
ADVGR Growth in real advances,
BKOFF Growth in number of bank
offices,
Das and Ghosh
(2007)
India
19942005
INEFF
Operating
expenses/total asset, NPRIOL Loans
NPL
to non-priority sector/ total loans,
SIZE
Interest
Log(bank
income
asset),
SPRD
less
interest
expense/total asset, CRAR Capital
(tier-I plus tier- II)/risk weighted
assets, PRM Income from loans/total
loans less call money rate
Yap (2011)
Indonesia
20012010
MAC
=macroeconomic
fundamentals -
RGDP growth-
LLP(Loan
growth in real GDP,
Inflation,
loss
Change in balance of goods and
provision)
services, Change in annual interest
rates, Financial markets= returns of
stock market, Exchange Rate
FS=Financial soundness indicators FA/GDP=Net foreign assets/GDP,
54
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
Changes in annual lending rates,
Changes in reserves of deposits
money banks
Stock index -
High Returns
-
measures peak performance of stock
market in a particular year,
Low
returns- measures worst performance
Bank
specific
indicators
-
INEFFICIENCY= Cost to income
ratio
SR (t-1), Change in GVA, Weighted
average lending rate(t-1), Exports to
GDP ratio (t-2), CPI (combined )
inflation) (t-1), Gross Fiscal deficit
to GDP ratio(t-2)
Kalirai
Scheicher
(2002)
LLP
and
Austria
=
1990-
Total loan Cyclical
2001
provisions/
Indicators
-
GDP,
Industrial production, Output gap
Total loans
Price stability indicators - Inflation,
M1
Household indicators –Consumption,
Unemployment,
Employee
compensation, New car registrations
55
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
Corporate indicators – Investment,
Total gross fixed capital formulation
(GFCF), GCFC construction- nonresidential,
GFCF
construction-
residential,
GFCF- machinery and
equipment,
Real
productivity,
Business
climate
index,
Bankruptcies
Financial
market
Nominal
3-month
indicators
interest
rate,
Nominal 10-year bond yields, Real
3-month interest rate, Real 10-year
bond yields,
ATX, DJIA, DAX,
Euro STOXX, Yield Curve
External
indicators
–
Exports,
ATS/USD Exchange rate,
GBP Exchange rate,
ATS/
ATS/ ITL
exchange rate, ATS/ CHF exchange
rate, ATS/ JPY exchange rate, Oil
price (north sea),
Oil price(Arab
light), Oil Price (brent crude, 1
month forward)
Hesse and Cihak OECD
1994-
(2007)
2004
Kearns (2004)
countries
Ireland
19822003
Z score
banking industry specific variables,
bank specific variables
LLR (loan Real
GDP
growth
rate,
loss
Unemployment rate, Earnings before
provision) taxation and provisioning, Annual
56
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
growth in stock of loans, Loans/
Total Assets, Total Capital/Total
Assets
Real
Bikker
and 29 OECD 1991-
Metzemakers
countries
2001
LLR (loan
loss
provision)
GDP
growth
rate,
Unemployment rate, Earnings before
taxation and provisioning, Annual
growth in stock of loans, Loans/
Total Assets, Total Capital/Total
Assets
Hanschel
and Switzerlan
Monnin (2005)
d
Market Price data - banks' stock
1987-
price index, yield spreads for bank-
2002
issued bonds
Balance sheet data -total interbank
deposits, return on assets of banking
sector, variation in bank capital,
banking sectors provision rates
Non-public data-total assets of banks
under scrutiny(disclosed by Swiss
federal banking commission)
Other structural variables -variation
in number of bank branches
Output gap –derived from Cobb
Hoggarth et al.
(2005)
UK
1993-
DV
write
2004
off ratio
douglas
production
Nominal short-term interest rate,
Annual
RPIX
exchange rate
57
function,
inflation,
Real
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
Vazquez (2012)
Brazil
20012009
NPL (logit GDP growth rate, total loans in bank
transformat credit portfolio, slope of domestic
ion)
yield curve
GDP,
Lakstutiene
et
al. (2015)
Lithuania
2001-
Probability
2008
of default
Household
consumer
spending, export and import of goods
and
services,
unemployment,
housing prices, net salary, loan
portfolio, interest for loans, inflation,
1990
Clair (2004)
Singapore
to
2003
Roy
and
Bhattacharya
India
(2011)
NPL/Total
Loan ratio
1995-
Default
2007
rate
Bankruptcy, GDP, Inflation, Interest
rate, Share market, Unemployment
rate
Output gap, Inflation rate (CPI),
REER, bank rate, repo rate, reverse
repo rate
Hong
Wong,
Choi, Kong and 1994-
and Fong (2008)
mainland
2006
Default
GDP growth rates , interest rates, and
rate
real estate prices
China
GDP growth, interest rates, corporate
1986
Virolainen
Finland
(2004)
to
2003
probability
of default
indebtedness,
inflation,
industrial
production, growth rate of real
wages, the stock price index and the
oil prices
Zeman
Jurca (2008)
and
Slovak
1995
NPL ratio
to
58
Cyclical
Indicators-
GDP,
Industrial production, Output gap
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
2006
Price Stability indicators - Inflation,
growth of M1 monetary Aggregate
Financial
Nominal
Market
and
indicators-
real
3
BRIBOR(Bratislava
mnth
Interbank
offered rate), SAX (Slovak stock
Index
External indicators - Export, oil
price, exchange rate SKK/EUR
Annual growth of GDP, Inflation
Kyriaki
and
Moutsianas
Greece
2001-
Loan Loss (CPI), Industrial Production index,
2013
Provision
Unemployment
rate,
economic
Chinese
currency
sentiment indicator
GDP
Rongjie
and
Yang (2011)
China
growth,
1985-
Default
exchange rate, nominal interest rate,
2008
rate
real
property
index,
CPI,
unemployment rate
Cylical
Boss
et
(2009)
al.
Austria
indicators,
Corporate
Household
1970-
indicators,
indicators,
2007
External indicators, Price stability
indicators and interest rates
Probabiliti
Burrows et al.
UK
2011
Real GDP, M4 lending, nominal
of GDP, nominal effective corporate
interest rate
default,
es
59
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
write-off
Aggregate
Default
Aikman et al.
(2009)
UK
1997-
pobabilitie
2007
s,
Loss
given
(2009)
Filosa (2007)
UK
Italy
19932005
and
10
yr
govt
bond
rate,
unemployment, Real house prices,
Income gearing, Corportae lending, 3
m LIBOR spread, 10 Yr corporate
spread, Real Oil Prices, Real world
default
Alessandri
Real GDP, CPI, 3 m-T-bill rate, yr
equity prices
Default
Real output, CPI inflation, real
probability
equity prices, overnight nominal
*Loss
interest rate, 20 yr nominal interest
given
rate, sterling-dollar real exchange
default
rate, oil prices
1990-
Log
of Output gap, Inflation rate, spread,
2005
default rate capital to loans ratio
Business activity - GDP growth
Havrylchyk
South
2001-
(2010)
Africa
2008
Quality of
loan
portfolio
Real GDP growth, GFCF Real
growth
in
gross
fixed
capital
formation, Index Change in All-share
index at the Johannesburg Stock
Exchange
Interest rate - Prime rate, Nominal
prime overdraft interest rate set by
the South African
Reserve Bank, Real prime overdraft
interest rate set by the South African
60
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
Reserve Bank
BA rate Real interest rate at which
banker's acceptances are traded
Prices – Inflation, Inflation without
housing costs, M1 growth, Growth in
M1 aggregate, M2 growth, Growth
in
M2
aggregate,
M3
growth,
Growth in M3 aggregate
Household
Property
sector-
Nominal growth in property prices,
Consumption
Growth
in
real
consumption, Debt/Income A ratio of
debt
to
disposable
income
of
households, Employment Change in
the
employment
index,
Wage
Change in wage index
External economy - Commodities
Change in commodity price index,
Oil price Change in oil prices, REER
Change in real effective interest rate,
Terms of trade Change in terms of
trade
Greece,
Castro(2012)
Ireland,
1997-
Portugal,
2011
NPL/Total
gross Loan
ratio
Spain and
Real
GDP
growth
rate,
Unemployment rate, interest rate,
overall credit growth, growth rate of
share price indices, REER, terms of
61
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
Italy
trade, inflation
GDP,
Bikker and Hu OECD
1979-
(2001)
1999
countries
Profits
Unemployment,
inflation,
share prices, M3, loans, interest
differential,
non-bank
deposits,
capital and reserves
Gerlach (2005)
Hongkong
1994-
NPL/Total
2002
Loan ratio
Macro: Economic growth, inflation
Financial variables: interest rates,
change in property prices
Bank specific: asset size, sectoral
concentration in lending
Boss (2002)
Austria
Amediku (2006)
Ghana
default
probability
19952005
NPL
same as Kalirai and Scheicher (2002)
REER, Imports, Inflation (CPI),
prime rate, Output gap
REER, CPI, Terms of trade index,
Tracy (2007)
Jamaica
1997-
NPL/Total
2006
loans ratio
Ratio of public to private loans for
commercial banks, loan stock, 180
day treasury bill rate, growth of M1
deflated by CPI
Hadad (2007)
Indonesia
1996-
Loan Loss
2005
Provisions
GDP, Inflation, growth of money,
Sertifikat Bank of Indonesia’s rate,
foreign exchange, gasoline prices,
62
Proxy for
Author (Year)
Country
Period credit
Macroeconomic variables
Risk
diesel fuel prices
Drehmann
(2005)
Quagiliariello et
al.
UK
19802003
Italy
Default
Output gap, inflation, three month
rate
interest rate and real exchange rate
Source: Compiled from research papers
63
CHAPTER 3
Research Objectives
Based on theoretical underpinnings and an extensive review of literature, this chapter
lays down the objectives of the research. Section 3.1 presents the Research Gap.
Section 3.2 aims to build up on the research gap and proposes the Research Questions
followed by Section 3.3 which states the Objectives of study.
3.1
Research Gap
As discussed in the review of literature, stress testing has become an integral tool to
examine the resilience of the financial systems across the globe. The importance of
stress testing can hardly be overemphasized with IMF and World Bank making it
mandatory for the systematically important economies of the world (including India)
to undergo financial stability assessment under FSAP every five years.
Stress
Testing, in fact, provides useful insights with respect to the micro prudential
supervision and macro prudential assessment of the financial system vulnerabilities.
Macro prudential surveillance enables quantifying the macro-to-micro linkages and
focuses on the linkages between macroeconomic variables and micro prudential
supervision of the banking sector (Foglia, 2009).
Currently, there is a lot of ongoing research in the field of macro stress testing at the
world level with IMF, Central banks and researchers constantly aiming to improvise
the existing models in all dimensions to cover a broader framework of risks and
capture the dynamism in the financial systems. A lot of significant changes have been
64
made in the assessment of financial stability through stress testing by incorporating
the learnings from the global financial crisis of 2007-08. However, it is one of the
modelling areas which still require a lot of further research and development due to
the ever-dynamic financial environment and constant exposure of the financial
systems to the vulnerabilities of the global economies (Foglia, 2009). Also, with the
increasing importance and credibility attached to this practice, it becomes necessary to
assess and review the existing underlying framework of stress testing.
In the Indian context also, RBI conducts the financial stability assessment and
publishes the Financial Stability Report (FSR) on a bi-annual basis and Stress testing
is an important part of FSR. The first FSR was published in March 2010. However,
there is a notable absence of research in the area of macro stress testing. There is a lot
of scope of research in this area as not much research been conducted due to lack of
publicly available uniform banking data. At the central bank level, a lot of
improvements have been made with regard to the macro stress testing exercise over
the last many years; however, there are many areas which can be further reviewed.
After a thorough review of the stress testing practices across the globe and the Indian
practices, a number of areas have been identified in which the stress testing
framework as adopted in the Indian context can be improved. We may not be dealing
with all these gaps in the present thesis. However, identification of gaps can provide
us further avenues for research.
Researchers worldwide have endeavoured to incorporate an array of macroeconomic
indicators which may affect the assessment of credit risk. However, again in the
65
Indian context, very few macroeconomic variables have been included. In the FSR of
December 2016, the variables include Change in Gross Value Added, Weighted
average lending rate, Exports to GDP ratio, CPI (combined) inflation and Gross Fiscal
deficit to GDP ratio. These variables do not capture the entire gamut of dynamics of
the financial system vulnerabilities. There are many variables like money supply,
exchange rate, trade variables, oil prices, financial market variables, unemployment
etc. which may have an impact on the banking system which have not been captured
by the central bank’s stress testing exercise. In fact, over the last many years, not
many changes have been introduced to make the model more inclusive. Therefore,
there is a need to re-examine the variables that may have an impact on the stress
testing exercise. The inclusion of many variables may make the credit assessment
model quite complicated and difficult to interpret. Therefore, the relevant factors need
to be assessed and accordingly the research can be conducted. The methodology for
the same has been discussed in the research methodology section.
Another area of focus can be the calibration of the stress scenarios. Presently, in the
methodology adopted by the central bank, the risk scenarios include a baseline
scenario and two adverse macroeconomic scenarios-medium risks (up to 1 standard
deviation) and severe risk (up to 2 standard deviations) based on last 10 years
historical data. Although it may be quite inclusive, as the risks are evolving, there is a
need to re-examine the potential scenarios to include a wider range of possibilities
that may not be historical. It can be done by employing Impulse Response Function
(IRF), Variance Decomposition Analysis (VDA) and calibrate more scenarios to
illustrate the dynamic characteristics of the empirical model.
66
In the Indian context, there is a huge gap in terms of the techniques of modelling
currently being used for performing stress tests. For investigation into the current
position of macro stress testing of credit risk in the Indian context, time series
technique (VECM model along with its variants) will be employed. The research will
be further supplemented by Impulse Response Function and Variance Decomposition
Analysis. In other countries, various integrated models are used - Systematic Risk
Model (SRM) (Boss et al., 2006), Risk Assessment Model of Systematic Institutions
(RAMSI – Bank of England) (Demekas, 2015, Burrows et al., 2012, Aikman et al.,
2009) ; Macro Financial Risk assessment Framework (MFRAF – Bank of Canada
(Gautheir and Souissi, 2012); Corelated Systematic Liquidity and Solvency Risk
(Barnhill and Schumacher, 2011) ; and Stress Test framework with interaction
between market and credit risk (Wong and Hui, 2009).
There is a huge scope for developing an integrated model, but in the Indian context,
availability of granular data is a major problem. Subject to availability of data, an
integrated model can be developed on the lines of the above models. These models
will have to be adapted in the Indian context as the “one size fits all’ approach does
not suit Stress testing because of differences in financial and macroeconomic
conditions across the globe.
Also, as part of stress testing exercise by RBI, the resilience of the Indian banking
sector is subjected to a series of macro stress tests for credit risk against
macroeconomic shocks. In fact, worldwide, most of the existing literatures on macro
prudential stress testing focuses on credit risk modelling. However, the financial crisis
of 2007-08 highlighted the importance of the linkages between credit risk, market
67
risk, interest rate risks and liquidity risks and how their interdependence can lead to
financial breakdown of the economies (Foglia, 2009). Hence, it is very important to
conduct stress testing with respect to other forms of risk which impact the banking
systems, viz., market risk, liquidity risk and interest rate risk. In fact, there is enough
scope to evolve a risk model that integrates the various risk types.
To make the stress testing practice more credible and robust, stress testing framework
should include various risk types. This is because all risks to some extent are
interrelated. When liquidity in the system is reduced, credit risks increase; market
risks impact interest rate risk which further impacts credit risks. Increase in credit risk
give rise to NPAs which affect liquidity. Therefore, it is important that an integrated
and holistic approach is adopted for undertaking stress tests. Various researchers
across the globe have expanded the macro-prudential stress testing practices to
include market risk, liquidity risks and interest rate risks.
However, in the Indian context, the Financial Stability Report deals with macro stress
testing with respect to ‘credit risk’ only. There is a scope for assessment of other risk
types, either individually or develop an integrated model for risk assessment.
However the data required for stress testing for other risks is not available publicly in
the Indian scenario which prevents us from conducting research in this area.
Nevertheless, this is an important research gap.
68
3.2
Research Questions
Within this given framework of stress testing, an attempt has been made to address
the following questions in this research:
a)
What are the most important macroeconomic determinants that affect credit
risk in the Indian banking system?
b)
What is the impact of changes in macroeconomic variables on financial
soundness indicator of credit risk in the Indian banks?
c)
How resilient is the Indian banking sector towards macroeconomic shocks as
simulated through our credit risk model?
3.3
Objectives of the Study
Based on the research questions, the following are the objectives of our study:
a)
To identify the main macroeconomic variables that affect the credit risk in the
Indian banking and investigate the dynamics between the Financial Soundness
Indicator reflecting credit risk and the identified macroeconomic determinants.
b)
To prepare a macroeconomic stress testing model that estimates the
relationship between macroeconomic variables and credit risk.
c)
To evaluate and assess the resilience of the Indian banking system by
reviewing the current macro stress testing methodology for credit risk
(conducted by Reserve Bank of India) with the aim of improving the existing
model.
d)
To calibrate the most relevant macro stress testing scenarios keeping in view
the existing dynamic vulnerabilities.
69
CHAPTER 4
Research Methods and Procedures
This chapter presents the research methodology that has been adopted in conducting
our empirical research. This chapter aims to lays down a blueprint to facilitate our
research objectives and answer the research questions which have been put to test.
Section 4.1 provides the scope of the study in terms of the macro stress testing
approach to be followed for the research along with the coverage of the financial
institutions. Section 4.2 elaborates on the data collection techniques which include the
sources of data and the period of study. It also describes the type of risk under
investigation along with the rationale for choosing that particular risk. It also
highlights the credit risk model on which our study is based. Section 4.3 presents the
operationalisation of variables that have been employed in our research along with the
data constraints and logical reasoning for taking the particular variables for
investigation. Section 4.4 puts forward the methodology and framework for the study
along with the rationale for the application of a particular econometric technique.
4.1.
Scope of the Study
4.1.1. Approach: As discussed in Chapter 2, to translate and map macroeconomic
shocks and scenarios into financial sector variables, there can be two approaches of
macro stress testing - Top-down (TD) or Bottom-up (BU).
70
a)
Top-Down Approach- Top-down exercises are conducted by the national
authorities to examine the impact of macroeconomic shocks on the financial
stability of the institutions of a country in a centralised manner by employing
macro-economic data or aggregated bank data under consistent methodology
and assumptions (Kearns, 2004; Cihak, 2007; Oura and Schumacher, 2012;
Henry and Kok, 2013).
b)
Bottom-up approach- Bottom-up exercises are conducted by individual
financial institutions by using their own data and their internal respective risk
models to estimate the impact to various scenarios using highly disaggregated
data (Moretti et al., 2008; Oura and Schumacher, 2012).
Top-down approach to stress testing has been used for this study.
4.1.2. Financial Institution Coverage
One of the most important decisions regarding macro stress testing is the coverage of
the relevant financial institutions that should be included for the analysis. Here it is
important to evaluate the scope of the research - whether to include a particular set of
financial institutions or the entire gambit of financial institutions like, banks, nonbanks, co-operative banks, insurance companies, pension funds, hedge funds, shadow
banks etc. This study is confined to the Banking system in India due to the following
reasons:
-
The area of financial stability is organically linked with banking stability.
Banking system is considered as a yardstick to determine whether an economy
is strong enough to withstand shocks (RBI Working Paper, 2013)
71
-
Banking system is the most dominant segment in the Indian financial system
with the commercial banks accounting for more than 64% of the total assets of
the financial system (IBEF, 2016).
-
Due to its strategic importance for all member nations, work on stress testing
with respect to the banking system is the most advanced at IMF.
-
In the current landscape of Indian banking industry, credit risk has emerged as
a very critical risk type. The banking stability indicator (BSI) as released by
RBI shows an increase in the credit risk pertaining to the banking sector due to
the continuous deterioration in the asset quality. (FSR, Dec 2016).
-
Data constraints - In the Indian context, there is a dearth of publicly available
data pertaining to non-banking segment of the financial system which makes it
very difficult to conduct research in the area.
In this current backdrop, the thesis will be focusing on macro stress testing of the
Indian banking sector.
4.2.
Data Collection
This section covers the sources of data and the time period of study.
4.2.1. Sources of Data
The study is based on secondary sources of data. As International Monetary Fund
(IMF), World Bank and Bank of International Settlements (BIS) have been
extensively involved in the research in the area of stress testing, majority of the
literature review has been done based on research from IMF, World Bank and BIS.
Apart from this, major sources of reading material include Financial Stability Reports
of the several countries with special focus on the reports issued by Reserve Bank of
72
India. Several Research papers in this field have also been retrieved from EBSCO,
Proquest, SSRN and Google Scholar.
The data pertaining to the variables has been collected from (Global Economic
Monitor (World Bank indicators); RBI-Statistical Tables Related to banking and
Handbook of Statistics on the Indian Economy and US Energy Information
Administration.
4.2.2. Period of Study
Another important aspect in the analysis of stress testing practices is the decision
regarding the time horizon that should be used in the analysis. In our study, the time
period of study is 1996 Q2 to 2016 Q4.
The following are the arguments for
choosing the said time period. Initially the year 1991 was taken as the commencement
year for the study because the year 1991 is an important year in the Indian financial
landscape as this year marked the launch of an era of Economic liberalisation. It
started a new era of Liberalisation, Privatisation and Globalisation. However, on a
closer perusal of pilot data, the sample date was revised from 1991 to 1996 due to
following reasons:
- Data pertaining to External indicators: A series of financial reforms related to
exchange regime were initiated in 1991, starting from downward exchange rate
adjustment by 9% and 11 % in 1991 to putting in place, liberalised exchange rate
management system (LERMS) in 1992 involving dual exchange rate system to
finally replacing it with unified exchange rate system in March 1993 (Dua and
Ranjan, 2012). This would also have a major impact on the external indicators.
Due to the introduction of such changes in the initials years after 1991 and time
73
taken for the exchange rate to get stabilised, it was prudent to take the data from
1994 onwards.
- Data pertaining to Financial Market indicators: Four indicators were examined
with respect to financial markets – Market Capitalisation of BSE, Market
Capitalisation of NSE, Market Capitalisation of World and Market Capitalisation
of US for our analysis. Out of these variables, Market capitalisation of NSE was an
important indicator as NSE is ranked as the largest stock exchange in India in
terms of total and average daily turnover for equity shares every year since 1995
based on SEBI data (NSE). Therefore it is an important variable for our analysis
However, NSE begun its operations in 1994 due to which the data is available post
1994.
- Data pertaining to Growth indicators: As the year 1991 marked the year of
economic liberalisation, there were lots of changes in the growth indicators post
liberalisation due to radical economic reforms. Therefore, it took time for the
economy to stabilise. Also, the quarterly data pertaining to GDP is available only
from 1996 Q2.
Therefore, the study is from year 1996 Q2-2016 Q4. Quarterly data has been taken
to make the analysis more robust.
4.2.3. Risk Modelling – Type of Risk under Investigation
An important element in stress testing is the choice of the type of risk to be stressed.
Credit risk is the leading source of risk for banks and it is very important to identify,
measure and control risk and determine the capital requirement against this risk
(Moretti et al., Wall, 20133 and BIS 2000). The main focus of Stress Testing at IMF
has also been on bank solvency risk and the most robust and comprehensive
74
framework has been developed on credit risk modelling. There is a growing body of
literature that also has started focusing on liquidity and interest rate risks, especially
after the lessons learnt from the financial crisis (Jobst et al., 2013). However, in the
Indian context, Credit risk has emerged as one of the key banking risks that must be
addressed. Also, other forms of risks have not been researched much due to
unavailability of data in the public domain. Given the importance of credit risk and its
impact on the financial stability, the study focuses on Credit Risk modelling. Credit
risk can be defined as:
“Credit risk is most simply defined as the potential that a bank borrower or
counterparty will fail to meet its obligations in accordance with the agreed terms”.
(BIS 2000; RBI, 2010). It is the possibility of losses associated with diminution in
the credit quality of borrowers or counterparties (RBI, 2010)”.
Credit risk depends on a number of variables. However, based on the review of
literature, an attempt has been made try to categorise these variables and derive a
crude form of credit risk model which in the later stages of the thesis will be refined
and investigated. The crude form of credit risk model that has been employed in the
study can be stated as
Credit risk t = f (growth indicators, price stability indicators, external sector
indicators,
financial
market
indicators, household indicators)
75
indicators,
Interest
rate
In the Macro-stress testing approach, it implies forecasting a measure of distress (Y,
here credit risk) under extreme assumptions for the set of macroeconomic variables
(Sorge, 2004). Historical data has been employed to evaluate the sensitivity of banks’
balance sheet to the various shocks to macroeconomic fundamentals and then prepare
a model. The estimated coefficients derived from the model are used to simulate the
impact of the possible stress scenarios on the financial systems in the near future. All
variables in the study are considered to be endogenous variables.
4.2.4. Credit Risk Modelling
As discussed in detail in the literature review section, there are two approaches to
estimate the link between credit risk and macroeconomic factors: Merton model
(1974) and Wilson model (1997 a and b).
The first approach is based on the work of Merton which uses the option pricing
approach to estimate the firms’ probability of default. In the model, the firm is
expected to default when the value of its assets falls below a threshold value of its
liabilities. In case of Wilson model, the default rate of an economic sector is directly
related to macroeconomic factors.
The credit risk model framework employed in the study is called Credit Portfolio
View (CPV) based on Wilson model (1997a and 1997 b). This model was further
employed by Boss (2002) and Virolainen (2004) for stress testing. Here, once the
credit risk model is estimated, it can be related to the default risk to macroeconomic
factors. Typically, in a credit risk model, there is a proxy for credit risk as a dependent
variable and macroeconomic variables are used as explanatory or endogenous
variables.
76
4.3.
Operationalisation of Variables
As mentioned previously, the study is based on Wilson model, which establishes a
relationship between a credit risk indicator and macroeconomic factors as systemic
risk drivers. Central banks and researchers use various macroeconomic variables to
measure the fragility and vulnerabilities of the banking system. In fact, the
macroeconomic environment is one of the most important indicators of a country’s
financial stability and the selection of macroeconomic factors is of prime importance
in the study of macroeconomic stress testing. As per BIS (2000) also, stress testing
should take into consideration economic cycles, interest rate and other market
movements, and liquidity conditions. After a thorough review of literature, a set of
endogenous macroeconomic variables that impact the credit risk have been finalised.
The first section describes the credit risk proxies that have been employed by
researchers followed by a review of the macroeconomic indicators.
4.3.1. Credit Risk Indicators
Credit risk is associated with the quality of loans and is expressed in terms of loan
performance. Worldwide, it has been seen that credit risk is one of the most important
category of risks which has an important impact on the financial stability. Also, it is
the most widely researched category of risk. While conducting research on
macroeconomic stress testing, researchers have taken various proxies for credit risk
while establishing the relationship between credit risk and macroeconomic
fundamentals. Some of these credit risk indicators have overlapping definitions. Also,
some authors have also used the terms interchangeably.
77
BIS guidelines on definitions of NPL (BCBS, 2016) state that there are significant
differences in how banks identify and report asset quality, also there are no consistent
international standards for categorising problems of loans. The terms like nonperforming, loss, write-off etc are used in different contexts across different
jurisdictions (BCBS, 2016). In light of above, the literature available on different
indicators of credit risk has been examined.
Cihak (2007) and Foglia (2009) divide the credit risk modelling into two categories:
one based on data on loan performance like Non-Performing Loans (NPLs), Loan
Loss Provisions (LLPs) and historical default rates; and the other based on microlevel data related to the default risk of the household and/or the corporate sector.
Melecky and Podpiera (2010) also identify two main measures of risk a) the nonperforming loans (NPL, LLP and respective migration rates) and b) probabilities of
defaults (PDs) and loss-given default (LGD) and correlation of asset performance for
individual credit portfolio components. This section reviews the different indicators of
credit risk as taken for research. The most widely employed credit risk proxies are:
i) Non Performing Loans (NPLs) / Non Performing Assets (NPA) Ratio – NPL
ratio is expressed as ratio of Non-Performing Loans to Gross Loans. This is one of
the most common indicators of credit risk. Cihak (2004) states that more than half
of the FSAP missions use NPL based approaches.
IMF (2006) defines NPL as “Loans (and other assets) should be classified as NPL
when a) payments of principal and interest are past due by three months (90
days) or more; b)interest payments equal to three months (90 days) interest or
78
more have been capitalized (reinvested into the principal amount), refinanced, or
rolled over (that is, payment has been delayed by agreement and c) evidence exists
to classify a loan as non-performing even in the absence of a 90-day past due
payment, such as when the debtor files for bankruptcy.”
RBI Master Circular (2016) defines NPA as “A non-performing asset (NPA) is a
loan or an advance where: a) interest and/or instalment of principal remain
overdue for a period of more than 90 days in respect of a term loan, b) the account
remains ‘out of order’ in respect of an Overdraft/Cash Credit (OD/CC), c) the bill
remains overdue for a period of more than 90 days in the case of bills purchased
and discounted, d) the instalment of principal or interest thereon remains overdue
for two crop seasons for short duration crops, e) the instalment of principal or
interest thereon remains overdue for one crop season for long duration crops, the
amount of liquidity facility remains outstanding for more than 90 days, in respect
of a securitisation transaction undertaken in terms of guidelines on securitisation
dated February 1, 2006, and f) in respect of derivative transactions, the overdue
receivables representing positive mark-to-market value of a derivative contract, if
these remain unpaid for a period of 90 days from the specified due date for
payment”.
NPL ratio is a standard measure of loan quality that has been widely used in
research to analyse banking sector performance. IMF (2006) includes NPL ratio in
the list of Financial Soundness indicators for macro prudential analysis (IMF, 2006
and Roy and Bhattacharya, 2011). Quagliariello (2003), Banerjee and Murali
(2015), Amediku (2006) and Zeman and Jurca (2008) have employed NPL ratio
79
for their analysis. The Greek Central Bank, The Croatian Central Bank, and The
Central Banks of Albania, Serbia and Bosnia and Herzegovina employ NPL ratios
for mapping risk (Melecky and Podpiera, 2010). Henry and Kok (2013) identify
NPL ratio as an important ‘balance sheet’ type indicator to assess credit risk.
ii) Default Rate –Default rate has been used by different researchers with different
meanings. Quagliariello (2009) defines default rate as the ratio of the amount of
loans classified as bad debts in the reference period to the performing loans
outstanding at the end of the previous one. Roy and Bhattacharya (2011) follow the
same approach for investigating the dynamic impact of the changes in the
macroeconomic variables on the default rate. Henry and Kok (2013) explain
default rate as the number of defaulting loans to the total outstanding loans. Wong
et al. (2006) and Filosa, (2007) in their respective researches also suggest a
significant relationship between default rate and macroeconomic factors.
According to them, default rate is measured as a ratio of the amount of loans which
have been overdue for more than three months to the total amount of loans. Van
den End et al. (2006) and Rongjie and Yang (2011) have employed two credit risk
indicators – Default Rate and LLP (discussed below), however, they define default
rate as the number of defaults relative to the population of the firms. Virolainen
(2004) has employed Default Rate as the credit risk indicator and has defined
default rate as the number of bankruptcy proceedings instituted divided by the
number of active companies. Bank of Canada, Bank of England, Bank of Italy,
Bank of Spain employ default rates for macro stress testing practices (Foglia,
2009).
80
Zeman and Jurca (2008) assume NPL ratio and default rate as synonymous.
Although the two indicators have been used interchangeably, ECB draft
guidelines on NPLs (referred as Non Performing Exposures –NPE) suggest that
NPLs is a potentially broader concept as compared to default as all defaulted
exposures are necessarily NPAs but NPAs can also include those exposures that
are not recognised as default (ECB, 2016).
iii) Write off Rates (WRO): Bank write-offs are the losses (net of recoveries)
suffered on loans by banks. Hoggarth (2005) has employed write-off rates as a
proxy for credit risk. He suggests that write-off ratio is the most direct measure of
banks’ fragility and is sensitive to downturn in economic activity. However,
Henry and Kok (2013) argue that Write-off rates can only be taken as an
additional measure of credit risk because write-offs reflect a delayed response to
credit risk and can be considered as the final step in banks’ process of recognising
credit losses.
Roy and Bhattacharya (2011) suggest that NPA ratio and write-offs can be used
interchangeably as indicators for explaining asset quality.
iv) Slippage Ratios (SR)-slippage ratio can be defined as a ratio of Fresh NPAs
(Slippages being fresh accretion to NPAs during a period) to Standard Advances
at the beginning of the period. RBI while examining the resilience of the Indian
Banking system in the Financial Stability Report employs slippage ratio as a
credit risk indicator (RBI - Financial Stability Report). In fact, Default Rate as
81
employed by Quagliariello (2009) and Roy and Bhattacharya (2011) is the same
as Slippage ratio.
v) Loan Loss Provisions (LLP) - LLPs are the provisions made with respect to
loans where the bank is doubtful about the borrowers’ ability to meet their
financial obligations. It is expressed as (Total loan loss provisions / Total loans).
It can be understood as a periodic expense for possible future loan losses (Yap,
2011). Kalirai and Scheicher (2002), Yap (2011), Van den End et al. (2006),
Quagliariello (2007) and Kosmidou and Moutsianas (2015) have employed LLP
as a proxy for probable future losses on account of credit risk. Kosmidou and
Moutsianas (2015) state that under International Accounting Standard 39, loan
loss provisions are determined based on an incurred loss model i.e. there is a
measurable decrease in the estimated future cash flows from a group of financial
assets and cannot reflect losses based on expected future events. Swiss National
bank and Deutsche Bundesbank also employ LLP ratio as the dependent variable
in their credit risk model (Foglia, 2009). Central Bank of Poland and Slovenia
also employ LLP for analysis (Melecky and Podpiera, 2010)
vi) Loan Loss Reserves (LLR) - A similar proxy for measuring credit risk is Loan
Loss Reserves (LLR). LLRs can be expressed as loan losses as relative to
outstanding NPLs. It is also referred to as ‘coverage ratio’ (Henry and Kok,
2013). Bikker and Metzemakers (2005) investigate stress testing with both loan
loss provisions and loan loss reserves as they possess different characteristics.
According to them, Loan Loss provision is a discretionary decision and reflects
the manager’s approach towards provisioning. However, Loan Loss Reserves
82
reflect actual expected loan losses as it shows the year-on year accumulated net
provisioning. Also, according to them, for auditors, management, analysis and
regulators, Loan Loss reserves provide more important information regarding the
credit portfolio’s quality.
The next set of credit risk indicators have been identified by Henry and Kok
(2013) and Marcelo et al. (2008) as the key parameters of credit risk. These are
Probability of default, Loss given default and Exposure at default or loss rate.
However, data constraints are there in employing these parameters as this data is
available to the Central banks only. Hence, not many researchers have actually
employed these variables for macro stress testing.
vii) Probability of default (PDs) - Probability of default implies the probability that
the debtor will fail to fulfil the obligations in one year. In their paper, Lakstutiene
et al. (2015) have taken the probabilities of default of loans to business clients,
mortgage loans and consumer loans. Boss (2002) has made an attempt to model
the default probabilities as a logistic function of the macroeconomic variables.
Elizondo, J. (2010) defines PD as the average percentage of obligors that default
in one year. Quagliariello (2010) states that PD is measured as the flow of new
bad loan over the stock of performing loans in the previous period.
viii) Loss given default (LGD) Elizondo, J. (2010) has defined LGD as the
percentage of exposure the bank might lose if the borrower defaults.
ix)
Loss Rate (LR) - LR is a product of PD and LGD (Henry and Kok, 2013).
83
As mentioned above, these measures of credit risks have overlapping definitions.
However, based on the objective of the study and the context of the study, any of the
dependent variables can be employed. If these definitions are examined on a
continuum with respect to time perspective, the most forward looking metric is
Probability of Default which is measured as x-days ahead and the other extreme of the
continuum is WRO which reflects when NPAs are written off.
In the Indian context, only the NPA data is available in the public domain, therefore
two variables based on NPA data for further analysis have been examined - Default
Rate (Incremental Gross NPAs (t)/ Performing Loans (t-1) and Gross NPA to Total
Advances Ratio. Both these variables reflect different aspects of credit risk. Default
Rate helps to analyse credit risk with respect to incremental loans in a particular year
and Gross NPA to Total Advances ratio focuses on the data of the current year. As
accretion of NPAs is a very critical indicator of the efficiency of credit risk
management, Default Rate has been taken as a proxy for credit risk for the further
research.
4.3.2. Macroeconomic Indicators
The categorisation of variables has been done based primarily on the work by Kalirai
and Scheicher (2002) and Boss et al. (2002) (subject to certain changes based on the
country under examination). The variables in each category have been further chosen
based on an extensive literature review.
4.3.2.1.
Growth/ Cyclical Indicators:
Cyclical or growth indicators characterise the general overall economic activity of a
country. The banks’ problem loans are closely related to the economic and business
84
cycle, i.e. behind every financial crisis there are macroeconomic factors like
downturns in economic activities (Salas and Saurina, 2002). These indicators are
expected to be negatively correlated with the NPL ratio
i)
GDP – GDP is one of the most important indicators of the strength of the macro
economy (Gadanecz and Jayaram, 2009; Quagliariello, 2006 and Kalirai and
Scheicher, 2002). It is the primary measure of the state of the aggregate economy
and measures the growth of all productive economic activities within a country at
a specific year’s prices (Yap, 2011). Das and Ghosh (2007) state that GDP is an
important macroeconomic indicator as it is highly informative of other macro
variables also.
GDP growth rate implies improved economic activity which further implies better
repayment by borrowers therefore reduced loan problems. Conversely, when the
growth slows down, there is a decrease in the cash inflows of the borrowers which
makes it difficult for them to pay interest and principal on bank loan (Kalirai and
Scheicher, 2002). Therefore, GDP growth rate is expected to have a negative
correlation with default rate or provisioning behaviour (Pain, 2003; Thiagarajan et
al., 2011, Kalirai and Scheicher, 2002, Bikker and Metzemakers, 2005; Wong et
al., 2006; Hadad et al., 2007; Quagliariello, 2007; Zemen and Jurca, 2008;
Kosmidou and Moutsianas, 2015 and Salas and Saurina, 2002). Havrylchyk (2010)
suggest that GDP growth rate is negatively correlated to credit risk but the impact
is very limited as compared to other risk factors.
85
The lag structure of GDP and GDP growth is also very important while defining
the relationship between loan losses and GDP growth rate as its impact on banks is
found to be long lasting (Das and Ghosh, 2007; Quagliariello, 2007). Kearns
(2004) argues that GDP growth rate does not affect the current level of
provisioning but affects provisioning with a lag of 1 year.
Rongjie and Yang
(2011) in their study do not find any significant relationship between NPL and
GDP growth while taking GDP as endogenous variable in their VAR framework.
ii) Output Gap: The output gap is an economic measure of the difference between
the actual output of the economy and its potential output. Potential output is
referred to as the production capacity of the economy i.e. the maximum amount
of goods and services an economy can produce at full capacity Positive gap
occurs when actual output is more than potential output which can happen in
case of high demand scenario. A negative gap occurs when actual output is less
than potential output (Jahan and Mahmud, 2013). Sometimes, researchers are
more interested in employing output gap as the variable as it enables them to
gauge whether the economy is underperforming (negative gap) or overheating
(positive gap) and its impact on the credit risk.
Several methods have been used by researchers to calculate output gap. One of
the most commonly used statistical method employed for measuring output gap is
Hodrick Prescott (HP) filter. This method has been employed for macro stress
testing by Amediku, (2006), Filosa (2007), Marcucci and Quagliariello (2008),
Roy and Bhattacharya (2011) and Banerjee and Murali (2015). Another method
86
employed by researchers is to estimate the production function and has been
employed by Hoggarth et al. (2005) for macro stress testing for UK.
Output gap is expected to be negatively related to the default rate which implies
that positive output gap decreases the default rate and vice versa (and Scheicher
(2002); Filosa, (2007); Kalirai; Zemen and Jurca (2008); Marcucci and
Quagliariello (2008)). It is expected to be negatively affected by the default rate
as good macroeconomic conditions make it easier for the borrowers to honour
their obligations. However, Roy and Bhattacharya (2011) state that output gap
has a delayed response on default rate. Marcucci and Quagliariello (2008) note
that also, due to feedback effect, the output gap also in turn may be affected by a
rise in the default rate.
iii) Gross Fixed Capital Formation: It is believed that the companies increase their
investment expenditures especially Gross fixed capital formation when the
economy is doing well. This may lead to an increase in the productivity gains of
the companies, which in turn, may have a negative impact on the NPL ratio
(Kalirai and Scheicher, 2002). Kanyinji (2014) and Vogiazas and Nikolaidou
(2011) have also used gross fixed capital formation in their study. Havrylchyk
(2010) has employed growth in gross fixed capital formation as a variable for
performing macro stress testing.
iv) Industrial Production/ Industry Value Added: Kalirai and Scheicher (2002)
suggest that Industrial production growth often leads the GDP growth cycle. As
such, higher industrial production growth is expected to reduce loan losses since
87
the economy is in a growth phase. Boss (2002) suggests that industrial production
is an important determinant of corporate default rate. Kosmidou and Moutsianas
(2015) have taken the Industrial Production Index (which is an index covering
production in mining, manufacturing and public utilities but excluding
construction) as a variable for stress testing the Greek banking system.
4.3.2.2. Price Stability Indicators
The most widely used Price stability indicators are inflation and money growth.
i)
Inflation: One of the important indicators of price stability is inflation. There are
various proxies that have been employed by researchers for this variable namely
Inflation (Boss, 2002;
Kalirai and Scheicher, 2002; Hoggarth et al., 2005;
Gerlach et al., 2005; Filosa, 2007; Zemen and Jurca, 2008; Havrylchyk, 2010;
Ghosh, 2014; Kosmidou and Moutsianas, 2015), Consumer Price Index - CPI
(Virolainen, 2004; Amediku, 2006; Tracey, 2007; Boss et al., 2009; Financial
Stability Report, December 2010; Roy and Bhattacharya, 2011; Rongjie and
Yang, 2011) and Wholesale price Index - WPI (Financial Stability Report,
December 2010; Banerjee and Murali, 2015). It is first important to define these
different terms. The below are the definitions as given by World bank (World
bank indicators)
“Inflation as measured by the consumer price index reflects the annual
percentage change in the cost to the average consumer of acquiring a basket of
goods and services that may be fixed or changed at specified intervals, such as
yearly”.(WB indicators)
88
“Consumer price index reflects changes in the cost to the average consumer of
acquiring a basket of goods and services that may be fixed or changed at
specified intervals, such as yearly. (WB)”
“Wholesale price index refers to a mix of agricultural and industrial goods at
various stages of production and distribution, including import duties. (WB)”
Some authors assert a negative relationship between inflation and default rate
(Boss, 2002; Hoggarth et al., 2005; Filosa, 2007; Zemen and Jurca, 2008;
Kosmidou and Moutsianas, 2015). They suggest that rise in inflation can reduce
the real value of the loan which enables the borrowers to repay it easily (Kalirai
and Scheicher, 2002; Gerlach et al., 2005; Zemen and Jurca, 2008; Havrylchyk,
2010; Ghosh, 2014). Kosmidou and Moutsianas (2015) suggest that this negative
relationship is due to overall improvement in the competitiveness of the economy
with increased profitability and thereby better repayment capacity. Conversely,
falling inflation may lead to increased loan defaults as the real cost of borrowing
has increased (Kalirai and Scheicher, 2002; Zemen and Jurca, 2008; Ghosh,
2014). However, it is also argued that inflation can have an adverse effect on the
real income of the borrowers which may hamper the repayment capacity of the
borrowers (Ghosh, 2014). It is also suggested that higher inflation may lead to
setting up of higher interest rates by central banks thereby increasing the
borrowers repayment burden (Havrylchyk, 2010).
Thiagarajan et al.. (2011) conclude that the current year inflation has a strong
positive influence on current NPA and lagged inflation had a negative influence.
89
The reason being that current year inflation may lead to cost of goods and
services increasing thereby adversely affecting the ability of borrowers to repay
debt. The author does not provide any reasoning for the relationship between
lagged inflation and NPA. Another view point is that inflation can influence the
nominal interest rates and thereby affect the ability of the borrowers to repay the
loans (Yap, 2011). Higher inflation may reduce the value of real interest rates and
further encourage economic activity thereby reducing default rates (Zemen and
Jurca, 2008).
ii) Monetary Aggregates (M1/M3) – In research pertaining to macro stress testing,
various measures of money supply growth have been taken for research, most
relevant being Broad money (M3) growth rate and Narrow money growth rate
(M1). The classification of money supply as M1 and M3 depends on the country
under study and the prevalent local practices. As per Reserve Bank of India (RBI,
Manual on Financial and Banking Statistics -2007).
1=
ℎ ℎ
+
3=
+
ℎ
ℎ ℎ
ℎ ℎ
1+
ℎ ℎ
=
ℎ
+
ℎ
+
ℎ
+
ℎ
′
ℎ
−
ℎ
90
Bikker and Hu (2002, Hadad et al. (2007) and Financial Stability Report- India December (2010) employ M3 growth rate for the analysis of credit risk. Similarly,
Kalirai and Scheicher (2002), Tracey (2007) Zemen and Jurca (2008) and Yap (2011)
have employed M1 growth rate. Some authors have taken both the measures together
(Boss, 2002; Havrylchyk, 2010).
Several authors remark that Money growth has a potential connection to inflation
(Kalirai and Scheicher, 2002; Hadad et al., 2007; Zemen and Jurca, 2008; Financial
Stability Report, December 2010; Havrylchyk, 2010). Hence, increased money supply
can affect inflation thereby affecting the credit quality of borrowers. Mileris (2012)
states that changes in money supply may lead to changes in GDP also, along with
price level changes thereby affecting default risk. However, Kanyinji (2014) does not
find the monetary aggregate M1 to significantly affect default risk.
4.3.2.3.
External Sector
The below section describes the external sector indicators that affect credit risk.
i) Exports of Goods And Services / Terms Of Trade: : Many researchers consider
exports as a significant factor in the credit risk model (Financial Stability Report,
December 2010; Lakstutiene et al., 2015) affecting the credit risk scenario. Kalirai
and Scheicher (2002) conclude that a reduction in exports can have an adverse
impact on the loan repayment ability of the export-oriented firms because of
reduced cash flows. Some authors consider exports as an important component of
GDP and suggest that macroeconomic shocks to exports can lead to financial
sector instability. They assert that growing exports can positively affect the
91
economy as a whole, thereby reducing the probability of defaults (Hilbers et al.,
2000; Zemen and Jurca, 2008).
Some papers have considered ‘terms of trade’ as a proxy for trade and they
suggest that a drop in ‘terms of trade’ makes imports more expensive for the
country which may further lead to an increase in the credit risk of the banks
(Castro, 2013).
ii) Oil Prices: Our study intends to explore the implications of oil shocks on the
stability of the financial system. Literature reveals that an increase in oil prices
represents a negative demand shock to the economy as a whole and can cause
overall household and business costs to rise thereby increasing the risks of default
by the borrowers. Therefore, an increase in oil prices is likely to be associated
with a deterioration of economic climate and thus greater credit risk (Kalirai and
Scheicher, 2002; Hadad et al., 2007). Roy and Bhattacharya (2011) suggest that
rising oil prices contribute to inflationary trends and an increase in interest rates
and thus have an indirect impact on NPA levels. The study does not take into
account this variable as an endogenous variable but highlights the importance
associated with this variable. Some authors consider that oil price has a major
influence on corporate sector credit risk levels, and being a direct cost for most of
the firms, it may have a negative impact on industrial production (Boss, 2002;
Havrylchyk, 2010; Roy and Bhattacharya, 2011). However, Virolainen (2004)
did not find oil prices as a significant variable affecting default risk.
92
iii) Exchange Rate / Real Effective Exchange Rate (REER) – Several authors
have viewed exchange rates as an important variable that affects the credit quality
of the loans. Hadad et al. (2007) suggest that the relationship between exchange
rate and credit risk is ambiguous and that it depends on the international trade and
capital account of the country (Hadad et al., 2007). Similar views are expressed
by Zemen and Jurca (2008) that the impact of exchange rate on default is
ambiguous as depreciation of domestic currency favours exporters and harms
importers. However, some studies suggest that in case of a large appreciation of
exchange rate, export oriented firms will have negative impact on the revenues
leading to higher probability of defaults because of reduced capacity to service
debt (Hilbers et al., 2000; Rongjie and Yang, 2011; Roy and Bhattacharya, 2011;
Ghosh, 2014). Applying a similar logic, if the exchange depreciates, the
borrowers who have taken foreign currency denominated loans may not be able
to service them as the debt service obligations may increase (Hilbers et al., 2000;
Yap, 2011; Ghosh, 2014). Hoggarth et al.. (2005) found little impact of exchange
rate on aggregate write-offs.
Another variant of Exchange rate is Real Effective Exchange Rate (REER) which
is the weighted average of a country’s currency relative to an index or basket of
other major currencies adjusted for the effects of inflation. An increase in REER
implies that the currency has depreciated in real terms against other currencies in
the basket of currencies that are used to form the REER index. The depreciation
makes that country’s exports cheaper and imports costlier in real terms and for
exports, it makes the country more competitive. The impact on the banking
system would be indirect and linked to whether the economy is export-led or
93
import-driven. If it is highly export-led, then the improved competitiveness
should improve the cash flows of companies and thus, improve the bank’s credit
performance. On the other hand, if the country is import-driven, the higher cost of
imports is likely to have a negative impact on these companies and therefore lead
to greater credit risk for the banks.
Some authors suggest that an increase in REER which implies appreciation of
local currency, reflects an increase in a country’s competitiveness in terms of
currency. This makes the goods produced in the country more expensive which
further weakens the competitiveness of export-oriented firms thereby adversely
affecting their ability to service debt. Hence, REER is expected to have a positive
relationship with default rate (Castro, 2013). Many other papers have also
employed REER in the credit risk model with similar results (Amediku, 2006;
Financial Stability Report, December 2010; Havrylchyk, 2010; Roy and
Bhattacharya, 2011).
4.3.2.4. Financial Market Indicators
Stock market index is an important indicator for predicting the financial situation of a
country. Some researchers suggest that the stock market index pattern is similar to the
cyclical trend of the economy and high returns for investor denotes lower credit risk
and low returns may signify slow economic growth which can further affect the
borrowers repayment capacity (Kalirai and Scheicher, 2002; Hadad et al., 2007; Yap,
2011). An increase in the country’s index is a reflection of country’s overall growth
and thus has a positive impact on the default rate (Boss, 2002; Zemen and Jurca,
2008; Havrylchyk, 2010; Castro, 2013; Ghosh, 2014).
94
In bullish markets, the net wealth of the borrowers may increase making it easier to
honour the financial obligations. This implies that there is a negative association
between appreciation of the stock market index and defaults. At the same time, the
appreciation in stock market index may make the collateral values to appear to be
higher and make the portfolios riskier, which implies a negative association
(Quagliariello, 2007). Filosa (2007) did not find any significant relationship between
stock indices and default rate.
Many researchers have taken the country’s respective stock market indices as a proxy
for a financial market indicator. In the Indian context, the following proxies for
financial market indicators are relevant:
i)
Market Capitalisation of BSE / NSE: As mentioned above, worldwide many
researchers have taken stock market indices of their respective countries as an
important variable determining the credit risk, however, in the Indian context, as
the research in the area of macro stress tress testing is still in a very nascent
stage, not many papers are available in this area. Also, as per the few papers on
this subject, none of them have employed stock market indices as an indicator.
However, RBI employs both Nifty and Sensex as determinants of credit risk.
BSE Sensex is Asia’s first and the fastest stock exchange in the world with the
speed of 6 micro seconds (bseindia.com), whereas NSE is the largest stock
exchange in India in terms of average daily turnover (nseindia.com). Given the
importance of both the indices, the market capitalisation of both the markets
have been taken for analysis. Instead of taking only the indices, market
95
capitalisation has been considered as it is more inclusive and reflective of the
financial markets.
ii)
Market Capitalisation of World / United States of America: Some
researchers contest that many stock markets of the large industrialised nations
have spill over effects across the global markets and hence, it becomes
imperative to examine the impact of such changes on the financial stability of a
country. The reasoning put forward is that rising stock markets implies higher
returns to investors eventually leading to lower credit risk. Also, rising stock
markets in one country could also lead to a capital flight from other countries,
leading to a fall in those stock markets and an increase in credit risk in such
countries. Hence, some authors have also employed Dow Jones Industrial
average as a variable along with their country’s stock market index (Kalirai and
Scheicher, 2002).
4.3.2.5. Interest Rate Indicators
Interest rates indicators have a significant impact on the stability of the banking
system. They represent the direct cost of borrowing and the fragility of the banking
system is affected by the ability of firms and households to service their debt (Clair,
2004). There are several studies that highlight the prominent relationship between
interest rate and default rate.
Some papers suggest that increase in interest rates would imply greater cost of
borrowing and greater possibility of loan defaults as the debt burden of firms and
households increase thereby reducing their ability to honour their debt obligations
(Kalirai and Scheicher, 2002; Quagliariello, 2007; Marcucci and Quagliariello, 2008;
96
Ghosh, 2014). Hence, a positive relationship between interest rate and default rate is
expected.
Various proxies have been taken as interest rate indicators. This section summarises
the various interest rate indicators taken by various researchers. The most prominent
interest rate indicators are Nominal bank loan interest rate (Rongjie and Yang, 2011),
Real interest rate (Clair, 2004; Virolainen, 2004; Filosa, 2007; Rongjie and Yang,
2011), Nominal short term interest rate (Boss, 2002; Kalirai and Scheicher, 2002;
Hoggarth et al., 2005; Financial Stability Report, December 2010), Nominal Long
interest rate (Kalirai and Scheicher, 2002; Boss, 2002; Financial Stability Report,
December 2010), Real short interest rate and Real long interest rate (Boss, 2002;
Kalirai and Scheicher, 2002).
The other indicators are Prime lending rate (Banerjee and Murali, 2015), Spread
between loans and deposit rates (Bikker and Hu, 2002; Das and Ghosh, 2007;
Quagliariello, 2007; Castro, 2013;) Changes in lending interest rate (Kalirai and
Scheicher, 2002; Banerjee and Murali, 2015; Lakstutiene et al., 2015) , nominal and
real prime interest rate and bankers’ acceptance rate (Havrylchyk, 2010), Short term
interest rate (T-bill rate)- Kalirai and Scheicher, 2002; Hoggarth et al., 2005; Zemen
and Jurca, 2008; Pesaran et al., 2006; Financial Stability Report, December 2010;
Castro, 2013), Long term interest rate i.e. 10 yr Gsec yield (Kalirai and Scheicher,
2002; Financial Stability Report, December 2010; Castro, 2013), 3 month Euribor
(Vogiazas and Nikolaidou, 2011) and Weighted Average lending rate (Quagliariello,
2007 ; Financial Stability Report).
97
In the Indian context, monetary policy instruments like Bank rate, Repo rate, Reverse
repo rate (Roy and Bhattacharya, 2011), have been shown to have a significant impact
on the financial system and are important benchmarks for other interest rates further
affecting the credit risks.
4.3.2.6.
Household Indicators
i) Unemployment, total (% of total labour force): Unemployment refers to the
share of the labour force that is without work but available for and seeking
employment (World Bank indicators). Unemployment is an important indicator
that can be linked to credit risk assessment (Clair, 2004; Lakstutiene et al., 2015).
Higher unemployment indicates that households may be unable or have difficulty
in paying back debts (Kalirai and Scheicher, 2002 ; Kosmidou and Moutsianas,
2015). Kearns (2004) in his research finds unemployment rate as the most
significant factor affecting the rate of provisioning. It is because an increase in
unemployment can lower the repayment ability of the borrowers and hence
increase in the credit risk of the banks.
Infact, Rongjie and Yang (2011) argue that unemployment rate has a prolonged
impact on the default rates. Bikker and Metzemakers (2005) consider
unemployment rate as a proxy for business cycle which also reflects the structural
imbalances of the economy. They hypothesize that unemployment follows GDP
growth with a lag. However, they did not find any significant effect of
unemployment on LLP.
98
4.4.
Macro Stress Testing
After having defined the scope of our research and operationalising the dependent and
explanatory variables that will be considered for our analysis, the next step is to
perform macro stress testing. These variables have been chosen on the basis of a
thorough review of the economic theory and empirical studies and can be considered
as the macroeconomic determinants of credit risk.
In this section, the stages of
construction of a credit risk model have been explained and thereafter stress testing is
conducted. There are two important stages in this model.
4.4.1. Construction of Macroeconomic Credit Risk Model
The first stage of our empirical analysis is the estimation of the econometric model
which will establish the relationship between credit risk as expressed by the Default
rate and macroeconomic variables. Further Default rate has been regressed against the
macroeconomic variables. The equation is examined for Indian banking system for
the period 1996Q2 to 2016 Q4 based on calendar year quarterly data. The basic form
of the model that will be adopted for the study can be defined as follows:
Credit risk (t) = f (growth indicators, price stability indicators, external sector
indicators, financial market indicators, interest rate indicators, household
indicators)
The following section describes the various stages of analysis.
1.
Descriptive Analysis
First and foremost, all the variables that have been identified in the literature have
been examined through descriptive analysis. The data has been taken annually for all
99
the variables from 1996 to 2016 (21 years). Although the final analysis has been done
based on the quarterly data, annual descriptive have been done to enable us to
understand the nature of all the variables. Also, the data is not available annually for
all the variables.
The figures have been taken with 2010 as the base year and are based on calendar
year. To facilitate comparison, the data of Default Rate and NPA to Advances Ratio
has been converted from financial year to calendar year.
The following are the variables which have been analysed annually in the descriptive
section.
a.
Credit Risk variables
i.
Default Rate
ii.
NPA ratio (Gross NPA/ Gross advances)
b.
Macroeconomic variables
i.
Growth indicators

GDP

GDP per capita

Gross Fixed Capital Formation (GFCF)

Industry Value added
ii.
Price stability indicators

Consumer Price Index (CPI)

Wholesale Price Index (WPI)

Broad Money (M3)
iii.
External sector variables

Trade (Export plus Import)
100

Brent Oil

WTI Oil

Exchange Rate (Rupee/Dollar)
iv.
v.
2.
Financial Market Indicators

Market Capitalisation of BSE

Market Capitalisation of NSE

Market capitalisation of World

Market Capitalisation of US
Interest rate indicators

Short Term Interest rate

Long Term interest rate
Reduction Of Variables
To capture the relationship between credit risk and the macroeconomic variables,
selection of suitable variables is essential. In the section ‘operationalisation of
variables’, the variables have been classified into six categories, namely Growth
indicators, Price stability indicators, External sector indicators, Financial market
indicators, Interest rate indicators and Household indicators (For a detailed discussion,
please refer to operationalisation of variables). However, for further analysis it is
important to reduce the number of variables and identify the factors that are
significant and affect the Default Ratio. Thereafter, a list of all the relevant factors for
the multivariate model can be formalised. The main justification for reducing the
number of variables is that in VAR/ VECM models, a limited number of variables can
be constructed in the model because inclusion of large number of variables will make
the model very complicated and difficult to interpret. It also reduces the power of the
model. Hence, it is prudent to employ a parsimonious VAR/ VeCM model.
101
Based on the extensive literature review and their relevance in the Indian context, the
final variables from each category to be taken for analysis have been identified.
Principal Component Analysis (PCA) / Factor analysis can also be employed for
reducing the data. PCA and Factor analysis are both methods that are employed for
data reduction. Therefore, in this case, instead of examining the impact of individual
variables on the default probability, a factor analysis/ PCA can be employed for all
the factors and use the resulting factors obtained by PCA as the input for our credit
risk model. This model enables integration of the variables into a new set of
parameters called ‘factors’ and allows retention of most of the information in the
given data set. It also facilitates exploitation of a large macroeconomic dataset to
identify potential data.
Rongjie and Yang (2011) and Boss et al. (2009) establish a credit risk model by using
PCA. However, an important limitation of the PCA model is that it will not allow to
understand the impact of individual macroeconomic variables on the default rate.
Therefore, PCA has not been employed as there will be loss of information with
respect to individual macroeconomic variables.
Correlation as the method of
reduction of variables has also not been employed as this leads to the problem of
serial correlation. Hence, the data has been reduced based on an extensive literature
review and by examining the importance of a particular variable in the Indian context.
After the reduction of the variables, the quarterly descriptive analysis of the final
variables is presented. The data has been described from 1996 Q2 to 2016 Q4 (83
quarters).
102
3.
Stationarity: Unit Root Tests
Once the final variables and a crude credit risk model has been finalised, the next step
involves testing for Stationarity. A key concept of the empirical work based on time
series analysis assumes that the underlying time series is stationary. It is the first step
in the Autoregressive process and involves detecting the order of integration of time
series variables. A stochastic process is said to be stationary if its mean and variance
are constant over time (Gujarati et al., 2012). Checking the data for Stationarity i.e.
testing the variables for unit root and checking the order of integration is very
important as non-stationary time series data can lead to spurious results which may
further lead to misleading inferences and conclusions.
A time series Yt is said to be stationary if:
E(Yt ) = constant for all t
Var (Yt ) = constant for all t; and
Cov (Yt , Yt+k) = constant for all t and all k≠0,
For the study, it is important for the series to be stationary. The main reasons are:
Firstly, use of non-stationary data can lead to spurious regressions. In such a case, if
regression techniques are applied to non-stationary data, the result may give us
significant coefficient estimates and high R2 but the variables may be unrelated and
results may be worthless. Secondly, the Stationarity or Non-Stationarity of a series
can strongly influence the properties and behaviour of a time series. For example: the
word “shock” is usually used to denote a change or an unexpected change in a
variable-in case of stationary series. “Shocks” to the system will generally die away
gradually, but in case of a non-stationary series, the persistence of “shocks” may be
103
infinite (Brooks, 2014). Thirdly, as the behaviour of a non-stationary time series can
be studied only for a particular time period (due to time-varying mean or time-varying
variance or both), it is not possible to generalise it to other time periods. Hence, the
purpose of forecasting may be forfeited (Gujarati et al., 2012). There are two
frequently used types of non-stationarity:
-
Random walk model with drift which can be represented as
=
-
+
+
(eq 1.1)
Trend-stationary process which can be represented as
=
where
+
+
(eg 1.2)
is a white-noise disturbance term (Brooks, 2014).
For Time series analysis, the most commonly used tests for checking stationarity are
Augmented Dickey Fuller (ADF) test, Phillip-Perron (PP) test and Kwiatkowski–
Phillips–Schmidt–Shin (KPSS) test. For Panel data analysis, the most commonly
used tests are Levin Lin Chu (LLC), ImPesaran Shin (IPS), Fisher-ADF, Fisher-PP,
and Breitung and Hadri.
The most commonly used time series methods of
Stationarity are:
- Dickey Fuller test (DF) – The initial and pioneering work on testing for unit roots
in time series was done by Dickey and Fuller (DF). The DF test is estimated in
three different forms, under three different null hypotheses. These forms are:
Yt is a random walk :∆
=
Yt is a random walk with drift: ∆
+
=
(eq 1.3)
+
104
+
(eq 1.4)
Yt is a random walk with drift around a deterministic trend
:∆
=
+
+
+
(eq 1.5)
where t is the time or trend variable (Gujarati et al., 2012).
In each case, the hypotheses are:
= 0 (i.e., there is a unit root or the time series is non
Null Hypothesis (H0):
stationary, or it has a stochastic trend)
< 0 (i.e., there is no unit root or the time series is
Alternative Hypothesis (H1):
stationary)
An important assumption of the DF test is that the error terms
are independentally
and identically distributed (iid) (Gujarati et al., 2012). DF tests may be biased due to
the presence of serial correlation (Mahadeva and Robinson, 2004) and were
augmented using ‘p lags’ of the dependent variable to ensure that the residuals were
not auto correlated. This test is called the Augmented Dickey Fuller (ADF) test
(Brooks, 2014).
- Augmented Dickey Fuller (ADF) test: As mentioned above, the ADF tests
adjusts the DF test to take care of possible serial correlation in the error terms by
adding the lagged difference terms of the regressand (Gujarati et al., 2012). The
ADF test involves estimating the following regression:
∆
=
+
+
+
105
∆
+
where
is a pure white noise error (Gujarati et al., 2012)
The null hypothesis of a unit root is rejected in favour of the stationary
alternative which further implies that the series is not stationary. The tests can be
conducted for an Intercept, Intercept and deterministic trend and no trend.
-
Phillips and Perron (PP) test - Phillips and Perron have developed a more
comprehensive theory of unit root non-stationarity. PP tests are very similar to
ADF tests, but they incorporate an automatic correction to the DF procedure to
allow for autocorrelation of residuals (Brooks, 2014).
In PP test also, the null hypothesis of a unit root is rejected in favour of the
stationary alternative implying that the series is not stationary. PP test is a
non-parametric test i.e, it assumes no functional form for the error process of the
variable. However, it relies on asymptotic theory which implies that it works well
with large samples.
As the analysis is a time series analysis, Augmented Dicky Fuller test (ADF) has been
employed to examine the stationarity of data. To increase the validity and reliability
of ADF test, PP test has also been performed.
4.
Determination of Optimal Lag length
In economics, the dependence of variable Y (the dependent variable) on another
variable X (explanatory variable) is rarely instantaneous. In a majority of the cases,
the impact of X on Y will be seen with a lapse of time. Such a lapse of time is called
106
“lag” (Gujarati et al., 2012). Therefore, a critical aspect of econometric studies is
estimating the lag length of the autoregressive process for a time series as the
inference of the model depends on the correct model specification. Lagged values of
the variables, both explanatory and dependent variables may capture important
dynamic structure of the regression equation (Chris Brooks, 2014).
There are several lag length selection criteria such as:
a)
Aikaike’s information criterion (AIC) (Akaike 1973),
b)
Final Prediction Error (FPE) (Akaike 1969),
c)
Schwarz Information Criterion (SIC) (Schwarz 1978),
d)
Hannan-Quinn Criterion (HQC) (Hannan and Quinn 1979),
e)
Bayesian information criterion (BIC) (Akaike 1979)
f)
Likelihood Ratio (LR)
Choosing of incorrect lag may reduce the forecast precision of the VAR model.
5.
Johansen Cointegration Test
After checking the Stationarity of data, if the variables are found to be integrated to
order I (1), the next step is to check for Cointegration or existence of a long term
relationship between the variables. The concept of cointegration was introduced in the
economic literature in a series of papers by Granger (1983), Granger and Weiss
(1983) by Granger (1981) and Engle and Granger (1987) (Watson, 1994; Kilian &
Lutkepohl, 2017). Two or more time series are said to be cointegrated if there is a
stable, long-term relationship between the two, even though individually, each may be
non-stationary (Gujarati et al., 2012).
107
Cointegration theory suggests that even if two time series are individually not
stationary, a linear combination of these two time series can be stationary. Hence, if
ther time series is non-stationary, Johansen Cointegration Test will be performed.
There are two statistical tests to check the number of Cointegration vectors - the Trace
(λ) test the Eigen value test. With respect to our research, Johansen Cointegration test
will help us to understand the long run equilibrium relationship between the credit
risk and the given set of endogenous macroeconomic set of variables if the time series
is non-stationary.
Lutkepohl and Kilian (2017) define Cointegration as “if two variables share a
common stochastic trend such that the linear combination of these variables is
stationary, they are called cointegrated.” This concept of cointegration may also be
applied to linear combinations of more than two I(1) variables (integrated to the order
1). Generalizing this concept to higher orders of integration, the variables in a Kdimensional process yt are cointegrated if the components are I(d) and there exists a
linear combination zt = β’yt with β = (β1, . . . , βK)’≠ 0 such that zt is I(d∗) with d∗ <
d. The vector β is called a cointegrating vector or a Cointegration vector.
Cointegration implies existence of a long run equilibrium and a common stochastic
trend. When two or more variables have a common stochastic trend, they will show a
tendency to move together in the long run and hence can be of considerable economic
interest (Juselius, 2006). It enables us to separate the short and long run variables and
thereby improve the long run forecast accuracy.
108
There are two statistical tests to check the number of cointegration vectors-
a)
The Trace (Λ) Test: The trace statistic is based on a likelihood ratio about the
trace of a matrix. The trace statistic considers whether the trace is increased by
adding more eigen values beyond the rth eigenvalue. The null hypothesis in
this case is that the number of cointegrating vectors is less than or equal to r.
b)
The Eigen Value Test - The eigen value test is based on the characteristic roots
(also called eigen values) obtained from the estimation procedure. The test
consists of ordering the largest eigen values in descending order and
considering whether they are significantly different from zero. The null
hypothesis is that there are cointegrating vectors and that there are upto r
cointegrating relationships and the alternate hypothesis is that there are (r+1)
vectors. (Asteriou & Hall, 2006)
6.
Vector Auto Regression (VAR) / Vector Error Correction Model (VECM)
The next stage is the implementation of VAR/VECM model to study the
macroeconomic effects on the Default Rate. If in step 5, while performing Johansen
Cointegration test, the variables are not cointegrated, VAR can be conducted.
However, if there is a long run relationship between the variables, i.e. the variables
are cointegrated, VECM model can be employed .
VECTOR AUTO REGRESSION (VAR): The use of VAR model for empirical
macroeconomics can be traced to the seminal work of Sims (1980). He demonstrated
that VAR provides a flexible and tractable framework for analysing economic time
series. In a univariate setting, an auto regression is a single linear equation model
109
where the variable is explained by its own lags. A VAR is an n-equation, n-variable
model where each variable is explained by its own lags and the lags and current
values of all other variables. VAR captures the relationship between variables without
imposing a theoretical structure (Stock and Watson, 2001; Quagliariello, 2009). VAR
model is expressed as a system of equations where each variable is expressed as a
linear function of its own lagged values and the current and lagged values of the other
variables incorporated in the model and a serially uncorrelated error term. Each
equation in the system can be estimated by using ordinary least squares (OLS). This
framework provides a systematic way to capture the dynamics in multiple time series
(Stock and Watson, 2001). There are 3 types of VAR:
o
Reduced form: A reduced form VAR expresses each variable as a linear function
of its own past values, the past values of the variables in the equation and a
serially uncorrelated error term. Each equation is estimated by OLS regression.
There are a number of methods which can help us to determine the number of
lags to be included in each equation (Stock and Watson, 2001). Most of the
studies on macro stress testing employ reduced form of VAR.
o
Recursive form: A recursive VAR constructs the error terms in each regression
equation to be uncorrelated with the error in the preceding equations. Hence, the
ordering of variables is very important in this form of VAR (Stock and Watson,
2001).
o
Structural VAR: A structural VAR uses economic theory to understand the
relationship between variables. In this technique, the assumptions need to be
clearly laid down to identify the causal link between the variables (Stock and
Watson, 2001). Reduced form VAR is based on a premise that the important
110
dynamic characteristics of the economy can be revealed without imposing
structural restrictions from economic theory. This has been often criticised by
researchers. This disapproval led to the development of Structural VAR which
allows transformation of economic theory into reduced form VAR model into a
system of structural equations. In this case, the parameters are estimated by
imposing contemporaneous structural restrictions. Such a VAR generally
provides responses which are consistent with standard macro-economic theory
(Keating, 1992).
VECTOR ERROR CORRECTION MODEL (VECM) : In case when two time
series which may/may not be related be integrated (non-stationary)and one such nonstationary time series is regressed on one or more non-stationary time series, it may
apparently reflect statistically significant relationships i.e., high R square, a very high
individual t-statistic and a low Durbin Watson statistic. But this relationship may be
spurious, i.e., there may be in reality no true relationship between them. However,
there might be a common stochastic trend to both series that a researcher is genuinely
interested in because it reflects a long-run relationship between these variables.
Vector error correction model (VECM) is a restricted VAR which is designed for use
with such non-stationary series that are cointegrated. Therefore, if the time series is
non-stationary, i.e I(1) and is cointegrated, VECM model can be run to examine both
the short-run and long-run dynamics of the series.
The VECM has Cointegration relations built into the specification so that it restricts
the long-run behaviour of the endogenous variables to converge to their cointegrating
relationships while allowing for short-run adjustment dynamics. The cointegration
111
term is known as the ‘error correction term’ since the deviation from long run
equilibrium is corrected gradually through a series of partial short-run adjustments.
VECM is an important econometric model as:
-
It measures the correction from disequilibrium of the previous period which has
important economic implications (as will be understood in the later section).
-
As the variables are cointegrated, the VECM incorporates both long-run and
short-run effects. This is because the long run equilibrium is included in the
model together with the short run dynamics captured by the differenced term.
-
VECM enables us to resolve the problem of spurious regressions as it typically
eliminates the trends from the variables involved as Cointegration ECMs are
formulated in terms of first differences.
-
An important implication of VECM is that as the variables are cointegrated, there
is some adjustment process which prevents the errors in the long run relationship
becoming larger and larger.
-
VECM is preferred to Engle-Granger two step procedure as VECM can detect
multiple long run stationary relationships among non-stationary variables. The
long run part of the VECM indicates a linear combination of non-stationary
variables lagged by one period that becomes stationary in nature. As suggested
above, VECM model enables to understand the long run and short run
relationships between the variables:
Long run relationship: The “error correction term (ect)” enables us to understand the
long run relationship between the variables. If the coefficient of ect is negative and
significant, it suggests that there is a long run relationship reflected by the model.
112
Short run relationship: VECM model also enables us to understand the short run
relationship existing in the model. The following tests have been conducted to
investigate the short run relationship between the variables.
- Wald Test: WALD test shows the joint significance of the lagged impact of the
endogenous variables on the Default Rate (DR). To support the results of WALD
test, the two way causality of the model is checked by performing the Granger
Causality Test.
- Granger Causality Test: To make the results more robust, the direction of
causality among the endogenous variables can be found by doing pair wise
Granger Causality test. Granger causality tests will be performed to determine
whether the lags of one endogenous variable will improve the forecasting of
another variable for the given model by examining the direction of the causality of
the variables.
- Toda – Yamamoto Test (Modified Wald): Wald tests and Granger causality tests
may have non-standard asymptotic properties if the VAR contains I(1) variables.
These problems can be overcome by performing the Toda & Yamamoto tests
which overfits the VAR order (by adding an extra lag) and ignores the extra
parameters in testing for Granger causality and enables us to overcome the
problems associated with standard tests, especially the problem of asymptotic
properties (Lütkepohl, & Krätzig, 2004) .
113
7. Robustness Of The Model
To ensure the robustness of the credit risk model, the residuals for serial correlation,
heteroscedasticity and normality need to be checked. The stability of the model is also
investigated by performing the CUSUM test.
4.4.2. Macro Stress Testing
Once the credit risk model has been established and the robustness of the model is
affirmed, the next step is to perform stress testing. The impact of shocks can be
investigated through Impulse Response Function (IRF) and Variance Decomposition
Analysis (VD). IRF and VD illustrate the dynamic characteristics of the empirical
model (Keating, 1992, Roy and Bhattacharya, 2011). It is important to understand that
IRF and VD Analysis are sensitive to ordering of variables.
1.
Impulse Response Function
To examine the banks’ responses to shocks, impulse-response functions that are
derived from the VAR/VECM model are examined. Using VAR to model the
dynamics of DR and macroeconomic variables enables us to carry out impulse
response function which is the stress test that is proposed in our study). In a VAR/
VECM model, as there large number of variables involved, it becomes difficult to
interpret the estimated model, especially when there are lagged variables, they may
have coefficients which change sign across the lags and thereby making it difficult to
examine the impact of the variables in the system. As VAR/ VECM model capture the
interactions between these variables, it allows us to undertake the classical impulse
response analysis. Therefore, to alleviate the problem of interpretation due to change
114
in the sign of lags, Impulse Response Function and Variance Decomposition can be
performed (Quagliariello, 2009; Chris Brooks, 2014)..
Therefore, once the long run and short run association(s) between the variables have
been established, the next step is to estimate the Impulse Response Function. Impulse
response function describes the influence of one shock in the endogenous variable on
other endogenous variables in the VAR/ VECM model (Rongjie and Yang, 2011).
IRF traces out the responsiveness of the dependent variables in the VAR/VECM to
shocks to each of the variables. So, for each variable from each equation separately, a
unit shock is applied to the error, and the effects upon the VAR/VECM system over
time are noted (Brooks, 2014). In simple terms ‘Impulse is a one standard deviation
shock to the error term’.
As mentioned above, the ordering of variables is integral for carrying out Impulse
Response Function. Cholesky ordering will be employed to decide the ordering of
variables in the VAR system.
2.
Variance Decomposition Analysis
The next step in the credit risk modelling is to obtain the Variance Decomposition.
Variance decomposition function is performed to identify the contribution of each
shock to changes in the endogenous variables (Rongjie and Yang, 2011). It gives the
proportion of the movements in the dependent variable that are due to their own shock
versus shocks to the other variables. A shock to a variable will directly affect that
variable itself apart from transmitting the effect to other variables in the system
(Brooks, 2014).
115
CHAPTER 5
Data Analysis- Estimation and Results
This chapter presents the data analysis and results of the macro stress testing of credit
risk in India from 1996 Q2 to 2016 Q4. Section 5.1 presents the construction of the
macroeconomic credit risk model which includes operationalisation and annual
descriptive analysis of the credit and macroeconomic variables, Reduction of
variables for the target model and quarterly descriptive analysis of the final variables,
checking the variables for Stationarity, determination of the lag length, conducting the
Johansen cointegration test, running the Vector Error Correction model along with
checking of the long run and short run relationship among the variables, followed by
checking the robustness of the model for serial correlation, heteroscedasticity,
normality and stability.
In Section 5.2, the model is subject to macro stress testing
using Impulse Response Function and Variance Decomposition Analysis.
5.1.
Construction of the Macroeconomic Credit Risk Model
5.1.1. Operationalisation and Descriptive Analysis of the Variables (Annual
Frequency)
In the previous section, based on the literature review, a large number of variables that
the researchers have taken for the analysis have been presented. In this section, the
selected endogenous variables have been described followed by their annual
descriptive analysis. It is very important to understand the nature of these variables to
further select the final endogenous variables for our parsimonious model.
116
As the variables may be defined by the researchers differently, first section presents
the definition of the variables as employed in the research. The variables have been
described below:
5.1.1.1 Credit Risk Variables
a) Default Rate (DR): DR represents the ratio of incremental loans of time period
(t) to performing loans in the previous period.
DR = Incremental Gross NPAs(t)/ Performing Loans (t-1)
where performing loans = Gross advances -Gross NPAs
The data for time period 1996 to 2015 has been retrieved from Handbook of
statistics of Indian Economy (RBI) and for 2016 from Statistical Tables related to
Banking (RBI). As the data had been given in the format of financial year, it was
first converted into calendar year data to enable comparison between other
variables which are based on calendar year.
b) Gross NPA to Total Advances Ratio (GNPA_ADV) : GNPA_ADV represents
the ratio of Gross NPAs to Total Advances. As the data for DR and GNPA_ADV
is similar, for time period 1996 to 2015 has been retrieved from Handbook of
statistics of Indian Economy (RBI) and for 2016 from Statistical Tables related to
Banking (RBI). Again, as the data had been given in the format of financial year,
it was first converted into calendar year data to enable comparison between other
variables which are based on calendar year.
117
5.1.1.2 Macroeconomic Variables
a)
Growth Indicators
GDP (constant LCU in million) (GDP) : GDP is the sum of gross value added by
i.
all resident producers in the economy plus any product taxes and minus any
subsidies not included in the value of the products. It is calculated without making
deductions for depreciation of fabricated assets or for depletion and degradation of
natural resources. The data has been retrieved from World Bank Indicators.
GDP Per Capita (constant LCU) (GDPCAP): GDP per capita is gross domestic
ii.
product divided by midyear population.The data has been retrieved from World
Bank Indicators.
iii.
Gross Fixed Capital Formation (constant LCU in million) (GFCF): Gross
Fixed Capital formation can be defined as land improvements , plant and
machinery, equipment purchases and construction of roads, residential, commercial
and industrial buildings. The data has been retrieved from World Bank indicators.
Industry Value Added (constant LCU in million) (INDVA): Industry value
iv.
added can be defined as the value added in mining, manufacturing, construction,
electricity, water and gas sector. The data has been retrieved from World Bank
Indicators.
b)
i.
Price Stability Indictors
Consumer Price Index (2010 = 100) (CPI): Consumer price index reflects
changes in the cost to the average consumer of acquiring a basket of goods and
services that may be fixed or changed at specified intervals, such as yearly. Data
are period averages. The data has been retrieved from World Bank Indicators.
118
ii. Wholesale Price Index (2010 = 100) (WPI): Wholesale price index refers to a
mix of agricultural and industrial goods at various stages of production and
distribution, including import duties. The data has been retrieved from World
Bank Indicators.
iii. Broad Money (current LCU in million) (M3): Broad money is the sum of
currency outside banks; demand deposits other than those of the central
government; the time, savings, and foreign currency deposits of resident sectors
other than the central government; bank and traveller’s checks; and other
securities such as certificates of deposit and commercial paper. The data has been
retrieved from World Bank Indicators.
b.
External Sector Variables-
i.
Trade (LCU in million) (Exports + Imports) (TRADE): Trade has been taken
as a sum of Exports and Imports. Exports of goods and services represent the
value of all goods and other market services provided to the rest of the world.
Imports of goods and services comprise all transactions between residents of a
country and the rest of the world involving a change of ownership from non
residents to residents of general merchandise, nonmonetary gold, and services.
The data has been retrieved from World Bank Indicators.
ii.
Oil (WTI) (Dollars per barrel) (WTIOIL): WTI is the benchmark crude for
North America. The data has been retrieved from US Energy Information
Administration.
iii.
Oil (Brent Crude) (Dollars per barrel) - (OIL):Brent oil is produced in the
brent oil fields and other sites in North Sea and is the benchmark for African,
European and Middle –eastern crude. The pricing mechanism for brent dictates
the value of roughly two-thirds of the world’s crude oil production. In India,
119
brent crude forms a part of the Indian crude oil basket. The data has been
retrieved from US Energy Information Administration.
iv.
Official Exchange Rate (LCU per US$, period average)-LCU_USD: Official
exchange rate refers to the exchange rate determined by national authorities or to
the rate determined in the legally sanctioned exchange market. It is calculated as
an annual average based on monthly averages (local currency units relative to the
U.S. dollar).The data has been retrieved from World Bank Indicators.
c.
Financial Market Indicators
i.
Market Capitalisation of BSE(LCU in millions) (MCAPBSE):Market
capitalization is the share price times the number of shares outstanding for listed
domestic companies in Bombay stock exchange. The data is as on December
closing. The data has been retrieved from Handbook of statistics on Indian
Economy (RBI).
ii. Market Capitalisation of NSE(LCU in millions) (MCAPNSE): Market
capitalization is the share price times the number of shares outstanding for listed
domestic companies in National stock exchange. The data is as on December
closing. The data has been retrieved from Handbook of statistics on Indian
Economy (RBI).
iii. Market Capitalization of Listed Domestic Companies (current US$)-USA
(MCAPUSA): Market capitalization is the share price times the number of shares
outstanding for listed domestic companies in US. Investment funds, unit trusts,
and companies whose only business goal is to hold shares of other listed
companies are excluded. Data are end of year values converted to U.S. dollars
using corresponding year-end foreign exchange rates. The data has been retrieved
from World Bank Indicators. As the growth rate is being examined, the data has
120
not been converted into local currency unitss as while converting of data, the
exchange rate effects may also reflect in the data.
iv. Market Capitalization Of Listed Domestic Companies (current US$)WORLD (MCAPWORLD): Market capitalization is the share price times the
number of shares outstanding for listed domestic companies worldwide.
Investment funds, unit trusts, and companies whose only business goal is to hold
shares of other listed companies are excluded. Data are end of year values
converted to U.S. dollars using corresponding year-end foreign exchange rates.
The data has been retrieved from World Bank Indicators.
d.
i.
Interest Rate Indicators
Long Term Interest Rate (LINTT): LINTT represents yields of SGL
transactions in government dated securities (G-Secs) for 10 years. G-Secs are
issued by RBI on behalf of government of India and are issued in a demat form
(SGL). The data has been retrieved from Handbook of statistics on Indian
Economy (RBI).
ii. Short Term Interest Rate (SINTT): SINTT represents yield of SGL
transactions in 14 day treasury bills. T-bills are issued by government of India
against their short term borrowing requirements with a maturity ranging between
14 to 364 days. The data has been retrieved from Handbook of statistics on Indian
Economy (RBI).
121
This section presents the descriptive analysis of the variables. The data has been taken
annually for all the variables from 1996 to 2016 (21 years). The figures have been
taken with 2010 as the base year and are based on calendar year. The data of NPA has
been converted from financial year to calendar year. Table 5.1 shows the descriptive
of the variables and their growth rates.
Table 5.1 Descriptive Analysis of Variables (Annual frequency)
Mean
Median
Maximum
Minimum
Std. Dev.
CREDIT RISK INDICATORS
DR
1.03
0.79
4.74
-0.80
1.38
GNPA_ADV
7.09
5.66
16.18
2.23
4.78
-1.22
-8.72
66.77
-35.25
23.20
G_GNPA_ADV
GROWTH INDICATORS
GDP (Rs in mn)
G_GDP
GDPCAP (Rs)
G_GDPCAP
GFCF (Rs in mn)
G_GFCF
65896928.56 60043137.34 121898539.34 31790240.41 28301367.51
7.00
7.51
10.26
3.80
2.05
55269.89
51673.23
92056.47
32475.70
18899.60
5.38
5.88
8.76
2.02
2.10
19722501.01 18579079.61
36020414.63
8.43
7021943.19 10276191.41
7.93
23.98
-1.39
6.41
19338504.17 18173670.65
34856707.41
9358444.60
8260652.49
7.21
12.17
2.61
2.54
83.94
69.87
154.95
41.00
36.26
7.01
6.37
13.23
3.68
2.99
84.91
78.23
129.96
48.42
28.20
5.02
4.82
9.56
-2.74
2.77
INDVA (Rs in
mn)
G_INDVA
6.81
PRICE STABILITY INDICATORS
CPI
G_CPI
WPI
G_WPI
M3 (Rs in mn)
G_M3
43128674.26 28958290.52 116176150.95
15.95
16.73
122
22.27
6233354.00 35926267.86
8.13
3.70
Mean
Median
Maximum
Minimum
Std. Dev.
51798333.20
6942331.57 17348037.40
EXTERNAL INDICATORS
TRADE (Rs in
mn)
28375624.78 28065799.81
G_TRADE
10.38
10.55
29.38
-5.61
9.56
LCU_USD
48.01
45.73
67.20
35.43
8.42
3.70
4.35
14.50
-8.74
6.07
55.83
52.32
111.63
12.76
34.08
8.62
11.17
60.11
-47.14
28.80
54.28
48.66
99.67
14.42
29.89
7.75
9.52
57.08
-47.77
26.81
G_LCU_USD
OIL ( $ per barrel)
G_OIL
WTIOIL ($ per
barrel)
G_WTIOIL
FINANCIAL MARKET INDICATORS
MCAPBSE (Rs in
mn)
G_MCAPBSE
41263359.22 31447680.00 106233470.00
23.45
18.01
102.70
4392310.00 35828932.14
-56.14
42.07
MCAPNSE (Rs in
mn)
G_MCAPNSE
MCAPUSA ($)
G_MCAPUSA
39841454.14 29167680.00 104396212.90
24.48
3911300.00 34883578.43
21.70
103.16
-55.42
42.03
16915419.13 15640707.04
27352200.72
8480497.00
5245867.15
30.09
-41.82
18.00
8.43
14.35
MCAPWORLD
($)
G_MCAPWORLD
41563896.55 40563282.86
8.67
64853776.19 19569896.01 15049347.03
13.38
38.22
-46.49
20.16
8.46
7.95
13.75
5.14
2.23
-1.62
-1.41
45.90
-32.07
17.90
SINTT
6.87
6.91
9.89
3.84
1.67
G_SINTT
1.65
-5.98
69.14
-31.70
28.18
INTEREST RATE INDICATORS
LINTT
G_LINTT
123
Credit Risk Indicators
This section examines both the proxies of credit risk i.e. DR and GNPA_ADV ratio as
both these ratios reflect a different aspect of credit risk. GNPA_ADV addresses the
NPA issue from the current year’s perspective i.e. current year NPAs divided by
current year Advances; however DR can be viewed with respect to incremental loans
or slippage of loans in a particular year. The average GNPA_ADV ratio from 1996 to
2016 is 7.09%, the lowest ratio recorded in the year 2008 as 2.23% and the highest
being 16.18% in the year 1996. Figure 5.1 displays the trends of DR and
GNPA_ADV graphically.
20
16
12
8
4
0
-4
96
98
00
02
04
DR
06
08
10
12
14
16
GNPA_ADV
Figure 5.1 Credit Risk Indicators
As can be seen in the graph, there is a gradual decline in the GNPA_ADV ratio from
16.18% (1996) to 2.23% (2008). However, post 2009 GNPA_ADV ratio again started
increasing and reached 9.13% in 2016. The GNPA_ADV ratio was as high as 6.67%
in 2015 and 9.13% in 2016. The mean DR was 1.03 in the sample period ranging
124
from -0.80 (2005) to 4.74 (1996). Similarly, the DR was 3.40 in 2015 and 2.80 in
2016. The detailed reasoning of the trends in DR is explained in section 5.1.3.
Growth Indicators
The GDP of Indian economy was Rs 121.89 trillion as on Dec 31, 2016. As it can be
observed from Table 5.1, the annual GDP growth rate for the given sample period
from 1996-2016 is around 7%. Figure 5.2 presents the graphical presentation of the
growth indicators with respect to DR.
25
20
15
10
5
0
-5
96
98
00
02
04
DR
G_GFCF
06
08
G_GDP
G_INDVA
10
12
14
16
G_GDPCAP
Figure 5.2 Growth Indicators
As it can be observed from the figure, there are phases in the trends of GDP growth
rate. The average GDP growth rate from 1996 to 2002 was 5.59%. The economy had
got liberalised in 1991 and this period can be considered as the stabilisation period.
However, the period between 2003 and 2007 was a very high growth phase and was
accompanied by the consolidation of key macroeconomic indicators. The average
GDP growth rate in these five years was around 8.83%. In 2008, GDP growth rate in
India saw a sharp fall due to the onset of global financial recession (from 9.80% in
125
2007 to 3.89% in 2008) which resulted in contraction in both internal and external
demand, withdrawal of funds by the foreign financial markets which led to a
reduction of liquidity in the market which impacted trade and other economic activity.
However, the Government and Central Bank undertook multiple expansionary steps
and monetary easing policy which enabled economy to rebound in end of 2009 and
2010. Post 2009, the GDP has been growing at an average of 7.48% (2010-2016). A
similar trend is observed in the GDP per capita. The average GDP per capita income
in the sample period is Rs 55269.89 growing at an average of 5.38%.
With respect to GFCF, initially from 1996 to 2000, average GFCF growth rate was
very less (5.64%); however similar to GDP trends, GFCF also increased at a rapid
speed between 2003 and 2007, growing at an average of 16.15%. The years 2010 and
2011 saw new capacity additions due to government initiatives due to which growth
of GFCF was very high, 11% and 12.25% respectively. However, the last few years in
our sample (2012-2016) show a very sluggish growth in GFCF (around 3.76% ), it has
considerably contracted due to weak internal and external demand, low existing
capacity utilisation (which discourages new capacity additions), inconsistent factory
output and the most important low industrial growth. The mean INDVA growth
during the period is around 6.81 % ranging from minimum growth rate of 2.61%
(2001) to maximum of 12.17% (2006).
Price Stability Indicators
In this section 3 variables have been described- Wholesale Price Index (WPI),
Consumer Price Index (CPI) and Broad Money (M3). Inflation in India is measured
in terms of Consumer Price Index and Wholesale Price index. It is the percentage
126
change in the price index over a specific period of time. In India, Consumer Price
Index (CPI) and Wholesale Price Index (WPI) are two major indices for measuring
inflation. The WPI measures the price of a representative basket of wholesale goods.
CPI is a measure of change in retail prices of goods and services consumed by defined
population group in a given area with reference to a base year. As seen in Table 5.1,
the average CPI for the sample period is 83.94 ranging between 41.00 (1996) and
154.95 (2016).The mean WPI for the period was 84.91, the maximum being 129.96
(in 2014) and minimum 48.42 (in 1996). Figure 5.3 shows the graph of price stability
indicators along with DR.
25
20
15
10
5
0
-5
96
98
00
02
04
06
DR
G_CPI
08
10
12
14
16
G_M3
G_W PI
Figure 5.3Price Stability Indicators
As it can be seen, initially, there was not much divergence between CPI and WPI,
however, post 2009 it can be seen the divergence and this is mainly due to the
composition of the two indices. The period between 1999 and 2005 showed a
moderate growth in CPI (average 4.08%) as well as WPI (average 4.87%). However,
after 2005, the monetary policy stance changed and as a result CPI and WPI growth
rates started increasing at 6.96% and 6.10% respectively on an average (2006-2008).
127
As mentioned above, post 2009, CPI and WPI also started showing divergence. The
year 2009 showed a sharp contrast between CPI and WPI.
The CPI growth was
10.88% and WPI growth rate was 2.35%. The main reason was that the main
component of CPI was food articles which saw a steep rise in food inflation due to
monsoon shock of 2009 and a sharp increase in Minimum Support Price (MSP) and
enhanced coverage under the Mahatma Gandhi National Rural Employment
Guarantee Act (MNREGA). Post-recession, the CPI growth rate reached double digits
(approximately 11%) in 2009-2010 as the monetary policy was in an expansion mode
to support growth recovery with the key policy rates falling.
Another important Price stability indicator is M3 which represents Broad money. The
money supply determines the liquidity in the system and also affects the availability
and cost of credit. The average growth rate in M3 in the sample period was 15.95%.
As it can be seen from figure 5.3, the growth rate of M3 was initially high during the
expansionary phase of development; however it fell from 1996 to 2001 after which it
started increasing. The growth rate was highest in 2007 (22.27%) after which it again
started falling.
External Market Indicators
Table 5.1 presents the descriptive analysis of the four External market indicators
namely LCU_USD, TRADE, WTIOIL and OIL. Figure 5.4 displays the growth rates
of these variables along with DR. An important variable among these is Oil. Oil is one
of the most actively traded commodities in the world and is extremely sensitive to the
geopolitical events.
128
80
60
40
20
0
-20
-40
-60
96
98
00
02
04
06
DR
G_LCU_USD
G_BRENTOIL
08
10
12
14
16
G_TRADE
G_W TIOIL
Figure 5.4 External indicators
As it can be observed in the descriptives section as well as the graph, the global crude
oil prices have been quite volatile. As the Indian economy is largely an oil importing
economy, the volatility of oil prices highly affects India’s macroeconomic
fundamentals such as fiscal deficit, current account deficit and inflation. It has an
impact on the exchange rate as reduction in the prices of the crude oil helps to reduce
the import bill which further helps in narrowing the Current Account Deficit (CAD)
and thus the currency also benefits from lower CAD on the back of reduced demand
for dollars required to fund the deficit. The most popularly traded grades of oil are
Brent North Sea crude (Brent Crude) and West Texas Intermediate (WTI). Figure 5.5
shows the comparative prices of Brent Oil and WTI Oil.
129
120
100
80
60
40
20
0
-20
96
98
00
02
DR
04
06
08
BRENTOIL
10
12
14
16
W TIOIL
Figure 5.5 Brent Oil and WTI Oil Prices
The prices of WTI oil are marginally less than the price of Brent oil but the price
anomaly between the two is a short run phenomenon. As India has Brent crude in its
crude oil basket, the oil prices with respect to Brent crude will be analysed. As it can
be observed, the oil prices in the 21 years of sample period have been quite volatile.
The maximum oil price was reported in the year 2012 at the rate $111.63 per barrel
and the minimum price was reported in the year 1998 at the rate of $12.76 per barrel.
As OIL is one of our variables in the model, it has been explained in detail in the
quarterly descriptive analysis.
Another important external sector indicator is Trade and the most important
determinant of export performance of any country is the global economic outlook. As
it can be seen, trade also follows the growth (GDP) trend. The average annual growth
in TRADE from 1996 to 2016 was 10.38% with the maximum growth being 29.38%
recorded in the year 2005 and minimum being -5.61% recorded in the year 2015. One
of the principal reasons for such a high growth rate in India in 2005 was due to the
130
impact of the phasing out of the WTO agreement on Textiles and Clothing (ATC) and
the end of the quantitative restrictions in early 2005 due to which India and China
made significant inroads in the international market share. The last 5 years (20122016) have witnessed a low growth rate in trade averaging around 0.87% mainly due
to global slowdown and deflationary factors.
Exchange rate, another key external sector indicator, has an impact on the other
external indicators also. The average LCU_USD rate in the past 21 years is 48.01
ranging between 35.43 in the year 1996 and 67.20 in the year 2016. The exchange rate
depends upon a number of factors like geopolitical conditions, trade, inflation, interest
rates and most important growth rates of the economies. Rising fiscal deficit,
domestic inflation and rising worth of USD (Federal Reserve’s decision to reduce
Quantitative Easing) are some of the key factors why rupee is depreciation vis a vis
dollar is discussed in detail in the later section.
Financial Market Indicators
In the section, the descriptive analysis of MCAPNSE, MCAPBSE, MCAPUSA and
MCAPWORLD has been presented. Figure 5.6 exhibits the trends in the above
mentioned variables. As it can be observed, MCAPNSE and MCAPBSE move almost
similarly with an average growth rate of around 24.48 % forMCAPNSE and 23.49%
for MCAPBSE. In the year 2003, the MCAPBSE growth rate increased significantly
due to increase in the stock price across the board. This was mainly because there was
an improvement in overall corporate earnings which was being reflected in the stock
prices along with large investments by FIIs. Infact, the World and US markets have
grown at a lesser rate with MCAPWORLD growing by 8.67 % and MCAPUSA
growing on an average by 8.43%.
131
120
80
40
0
-40
-80
96
98
00
02
04
06
DR
G_MCAPNSE
G_MCAPW ORLD
08
10
12
14
16
G_MCAPBSE
G_MCAPUSA
Figure 5.6 Financial market indicators
Interest Rate Indicators
Interest rate indicators play an important role in the analysis of the credit risk. Figure
5.7 exhibit the trends in SINTT and LINTT. The mean SINTT in the sample period is
6.87% ranging from 3.84% (in 2009) to 9.89% (in 2000). Meanwhile, the average
LINTT is 8.46% with the maximum being 13.75% in the year 1996 and minimum
being 5.14% in the year 2003. Figure 5.7 shows the Interest Rate indicators along
with Default rate.
As it can be seen in Figure 5.7 the SINTT normally remains more than LINTT. The
reasons for the same have been explained in detail in the quarterly descriptive analysis
section.
132
80
60
40
20
0
-20
-40
96
98
00
02
DR
04
06
G_LINTT
08
10
12
14
16
G_SINTT
Due to non-availability of data for LINTT and SINTT available for 1995, for calculating
growth rates for the year 1996, T-bill rate of April 1996 (instead of Dec 1995) and 10 yr
Gsec of May 1996 (instead of Dec 1995) have been taken for calculations
Figure 5.7 Interest rate indicators
5.1.2. Reduction of Variables - Operationalisation of Final Variables for Target
Credit Risk Model
The previous section describes the variables that have been identified from the
literature that can be taken for our model. However, for a parsimonious time series
model, it is required to identify the variables that have a major impact on the default
rate as a VAR/ VECM framework cannot accept these many variables.
Based on the extensive literature review, their relevance in the Indian context and for
the reasons described below, the following variables have been identified from each
category that will be taken for analysis. To make the analysis robust, all the variables
are quarterly.
133
DEFAULT RATE (DR)-Default Rate of quarter ‘t’ can been defined as the ratio of
incremental gross NPAs in quarter ‘t’ to performing loans in quarter ‘t-1’. Performing
loans in a certain period is the difference between gross advances and gross NPAs in
that period. The definition of Default rate has been adopted from Drehmann (2008)
and Marcucci and Quagliariello (2008). RBI also takes slippage ratio as a proxy of
credit risk which can be defined as the fresh accretion to NPAs during a period i.e.
Slippage Ratio = Fresh NPAs / Standard Advances at the beginning of the period.
Accretion to NPAs is a very critical indicator of the efficiency of credit risk
management of the financial institutions. It is important for the banks to reduce fresh
additions to NPAs to improve the quality of their asset portfolio. A sharp decline in
the incremental NPAs is critical as it reflects a significant improvement in credit
appraisal, improved risk management and better resource allocation process. Hence,
DR can be considered as an important proxy for measuring the credit risk position of
a country’s banking system.
As the data for NPAs was available annually only, the annual data has been converted
into quarterly data. Quadratic match-sum option has been used for interpolation of the
data. This method fits a local quadratic polynomial for each observation of the low
frequency series and uses this polynomial to fill in all observations of the high
frequency series associated with the period. The quadratic polynomial is formed by
taking sets of three adjacent points from the source series and fitting a quadratic so
that either the average or the sum of the high frequency points matches the low
frequency data actually observed (Eviews 8).
134
Also, the data given in RBI was based on financial year and the data for other
variables is based on calendar year. Therefore to enable comparison, the annual data
of Gross NPAs and Gross Advances into has been converted into quarterly data
according to calendar year and thereafter the default rate has been calculated.
Default Rate = {Incremental Gross NPAs (t) / Performing loans (t-1)]
Growth Indicators- GDP - GDP is the most important indicator among the growth
indicators. GDP (market prices) at constant prices in the local current unit (Rs in
million) has been taken for analysis. Constant prices have been taken as current series
are influenced by the effect of price inflation and constant series are used to measure
the growth of a series, i.e. adjusting for the effects of price inflation. The base year for
the data is 2010. The data is seasonally adjusted as most of the official statistics are
often adjusted to remove seasonal components as it enables us to understand better the
underlying trends in the economy.
Price Stability Indicators – Consumer Price Index - Over, the last few years,
inflation has emerged as one of the main concerns of the policymakers in India as
inflation rate has important implications on the policy decisions. Till April 2014,
Wholesale Price Index (WPI) was the main index for measurement of inflation in
India; however RBI adopted CPI (combined) as the key measure of inflation after
that. Also, conceptually, as CPI is related to retail consumer and better captures the
market dynamics, it is a better indicator of inflation for guiding monetary policy
decisions than WPI. Therefore, CPI has been taken as the measure of inflation. The
data has been adjusted seasonally.
135
Financial Market Indicators – Market Cap NSE-NSE is ranked as the largest stock
exchange in India in terms of total and average daily turnover for equity shares based
on SEBI data (NSE). Therefore it is an important variable for analysis and hence log
of market capitalisation of NSE has been taken as the variable for analysis. To make a
parsimonious model, the other market capitalisation variables have not been taken.
External Sector Indicators: Two important external indicators have been identified
based on the literature review which has an impact on credit risk.
-
Exchange Rate – Foreign exchange rates play a critical role as part of the
external sector variables. It is a key variable that affects decisions made by
exporters, importers, bankers, businesses, financial institutions and policymakers.
Movements in exchange rates have very important implications for the
economy’s business cycle, trade and capital inflows. Infact, India’s exchange rate
management and monetary policy are closely linked as RBI is empowered to
control and deal with forex. In view of the importance of exchange rate, it has
been taken as the variable for further analysis.
- Oil Prices (Brent Crude)- Apart from exchange rate, oil prices have been taken as
a variable. The importance of Oil on the Indian economy has been widely
documented and researched. An increase in oil prices represents a negative demand
shock to the economy as a whole and can cause overall household and business
costs to rise thereby increasing the risks of default by the borrowers. Also, as Brent
crude forms a part of the Indian crude basket, the impact of Brent oil prices on the
credit risk has been exmained. Henceforth, OIL means Brent oil.
136
Interest Rate Indicators – Interest rate indicators play an intrinsic role in India’s
economic system, hence it is important to examine the impact of both short term and
long term interest on the credit risk.
- Short-Term Interest Rate - For the study, the yield of SGL transactions of 14 day
treasury bills has been employed as representing short term interest rate. T-bills
are money market instruments that are issued by government of India against their
short term borrowing requirements with a maturity ranging between 14 to 364
days. The yields of 14 day T-bill rates serve as short term benchmarks for interest
rates.
- Long Term Interest Rate –For the analysis, yields of SGL transactions in
government dated securities (G-secs) for 10 years has been taken as a proxy for
long term interest rate. G-secs are issued by RBI on behalf of government of India
and are issued in a demat form (SGL). Normally, the dated G-secs have a period of
1 year to 20 years. 10 year G-sec rate has been taken to reflect the long term rate of
interest. Many lending rates are referenced to 10 year G -sec yield. Any major
movement in the 10 year G-sec rates may affect the economy. Table 5.2 exhibits
the final variables along with the acronyms and the source of data.
137
Table 5.2 Operationalisation of Variables
Acronym
Variable
Source
Definition
Incremental
Gross
RBI - Handbook of Performing
Loans
NPAs(t)/
(t-1)
where
Statistics on Indian performing loans = Gross advances
DR
Default Rate
Economy*
-Gross NPAs
GDP is the sum of gross value
added by all resident producers in
the economy plus any product taxes
and
minus
any
subsidies
not
included in the value of the
products. It is calculated without
Log of GDP at
making deductions for depreciation
market prices,
of fabricated assets or for depletion
constant 2010
and
degradation
of
natural
LCU, millions, World Bank-Global resources. Data are in constant local
Ln_GDP
seas. adj.
Economic Monitor
currency.
Consumer
price
index
reflects
changes in the cost to the average
consumer of acquiring a basket of
goods and services that may be
fixed or changed at specified
intervals, such as yearly. The
CPI
CPI
Price, World Bank-Global Laspeyres formula is generally
seas. adj.
Economic Monitor
used. Data are period averages.
Official exchange rate refers to the
Official
exchange
exchange rate,
LCU_USD
rate
determined
by
national authorities or to the rate
LCU per USD, World Bank-Global determined
in
the
legally
period average Economic Monitor
sanctioned exchange market. It is
138
Acronym
Variable
Source
Definition
calculated as an annual average
based on monthly averages (local
currency units relative to the U.S.
dollar).
OIL
Brent
Oil, US
Dollars
per Information
barrel
Energy
Administration
Price of Brent Oil in dollars per
barrel
The share price times the number
Log of Market RBI - Handbook of of shares outstanding for listed
Ln_MCAP
Capitalisation
Statistics on Indian domestic companies in National
NSE
of NSE
Economy
stock exchange.
RBI - Handbook of Yield of SGL transactions in
Long
LINTT
term Statistics on Indian Government dated securities for 10
Interest Rate
Economy
year maturities
RBI - Handbook of Yield of SGL transactions in
Short
SINTT
term Statistics on Indian Treasury
interest rate
Economy
bills
for
residual
maturities upto 14 days
* The data for time period 1996 to 2015 has been retrieved from Handbook of statistics of Indian
Economy (RBI) and for 2016 from Statistical Tables related to Banking (RBI) . The data has been
given in the format of financial year which has been converted to calendar year to enable
comparison between other variables which are based on calendar year.
** GDP and McapNse have been transformed into natural log . DR, LCU_USD, OIL, SINTT and
LINTT have been taken as it is.
5.1.3. Descriptive Analysis (Quarterly Frequency)
This section presents briefly describes the final variables along with their growth rates
for the selected sample for 83 quarters from 1996 Q2 to 2016 Q4 (calendar year)
Table 5.3 exhibits the descriptive statistics.
139
Table 5.3 Descriptive Analysis (Quarterly frequency)
Variables
Mean
Median
Maximum
Minimum
Std. Dev.
DR
0.22
0.17
1.72
-0.32
0.34
GNPA_ADV
6.98
5.07
16.35
2.22
4.61
16387410 15024950
30632000
7853310
6778399
GDP (Rs in mn)
CPI
87.54
74.68
158.36
42.62
35.37
LCU_USD
48.40
46.04
67.86
34.94
8.42
OIL ($ per barrel)
56.35
48.25
132.32
9.82
34.53
37667782 28961940
108660631
3911300
32993101
MCAPNSE (Rs in mn)
SINTT
6.67
6.80
10.47
2.90
1.67
LINTT
8.65
8.01
13.96
5.14
2.18
LN_GDP
16.53
16.53
17.24
15.88
0.42
LN_MCAPNSE
16.90
17.18
18.50
15.18
1.15
-67.30
-1.28
487.61
-3915.16
451.13
-0.42
-1.83
28.12
-14.13
6.40
G_GDP
1.67
1.62
5.31
-1.42
1.28
G_CPI
1.62
1.50
4.52
-2.54
1.13
G_LCUUSD
0.87
0.38
11.19
-7.21
3.41
Transformed variables
Growth rates**
G_DR
G_GNPADV
140
Variables
Mean
Median
Maximum
Minimum
Std. Dev.
G_OIL
2.97
4.07
47.42
-58.91
17.57
G_MCAPNSE
4.89
3.61
53.05
-25.75
15.28
G_SINTT
1.11
-1.31
55.38
-45.17
17.72
G_LINTT
-0.47
-0.68
32.79
-39.18
8.01
** the growth rates have been calculated for 82 quarters(1996 Q3 to 2016 Q4)
DEFAULT RATE/ GNPA TO ADVANCES
The average DR for the sample period was 0.22 ranging between -0.32 (2014 Q2) to
1.72 (2015 Q2). It is also important to note that the mean GNPA_ADV is 6.98% with
a range of 2.22 (2008 Q1and Q2) to 16.35% (1996 Q2). It can been seen that there is a
substantial reduction in the GNPA_ADV over the past few years which continued
falling till 2012 Q2 after which again it started rising gradually and reached 9.83% in
2016 Q4.
Figure 5.8(a) shows the Quarterly DR followed by Figure 5.8(b) which displays the
Quarterly GNPA_ADV ratio and Figure 5.8 (c) which shows the Quarterly Growth
rate of GNPA_ADV ratio. As observed in Figure 5.8 (b), the GNPA_ADV ratio
exhibits a downward trend from 1996 Q2 till 2008 Q2 after which it starts rising
gradually. After 2015 Q2, the ratio starts increasing very rapidly.
141
DR
2.0
1.6
1.2
0.8
0.4
0.0
-0.4
96
98
00
02
04
06
08
10
12
14
16
14
16
Figure 5.8 (a) Quarterly Default Rate (DR)
GNPA_ADV
18
16
14
12
10
8
6
4
2
96
98
00
02
04
06
08
10
12
Figure 5.8 (b) Quarterly Gross Non Performing to Advances ratio (GNPA_ADV)
142
G_GNPADV
30
20
10
0
-10
-20
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.8 (c) Quarterly Growth Rate of GNPA_ADV
The primary reason for a sustained improvement in the NPA ratio and Default rate till
2008 Q2 was due to initiation of several reforms by the government and Reserve
Bank of India. Due to liberalisation and opening up of banking to private sector, there
was an increased competitiveness among the banks which lead to an improvement in
lending and credit management practices by the banks. The measures taken by RBI
and Government to expedite the recovery of NPAs included formation of Debt
Recovery Tribunals (DRTs), Asset Reconstruction Companies (ARCs), Corporate
Debt Reconstruction (CDR) mechanism, and the Securitisation and Reconstruction of
Financial Assets and Enforcement of Security Interest (SARFAESI) Act. This led to a
gradual acceleration in the growth of credit and a significant improvement in the
growth of gross NPAs. However, post the recession of 2008, the credit risk ratios
started increasing again due to pressures from the global financial crisis. The
economy slowed down due to which the demand contracted, foreign investors pulled
out of the economy and created a liquidity crunch, banks became very cautious about
lending which further led to unutilised capacities which were expanded during the
143
boom time. The additional capacities funded mainly by debts from banks which were
built up prior to the recession of 2008 remained underutilised due to the drop in the
demand across sectors as a result of recession adding to the stress in the system. This
led to defaults in the industrial sector mainly infrastructure, civil aviation, textile, iron
and steel in particular. Apart from economic slowdown, the repeated restructuring of
corporate loans called “evergreening” also led to the accumulation of NPAs. This led
to the persistence of “Twin Balance sheet problem” - over-indebtedness in the
corporate and banking sectors which implied that both banking and corporate sector
were under stress.
The year 2015 saw a sudden spurt of NPAs as RBI started conducting the Asset
Quality review (AQR) following which banks cleaned up their books, which led to
declaration of large accumulated NPAs. In the case of both GNPA_ADV and DR,
post 2015, the ratios increased at a very high rate. As per the latest Economic Survey
2016-17 India’s current NPA ratio is higher than any other emerging market (with the
exception of Russia) (Economic Survey 2017).
Ln_GDP
The average quarterly GDP at market prices of the sample period is Rs. 16387410
million growing at a quarterly rate of 1.67 percent from 1996 Q3 to 2016 Q4. India
has emerged as the fastest growing major economy in the world as per the Central
Statistics Organisation (CSO) and International Monetary Fund (IMF) and is expected
to be one of the top three economic powers of the world over the next 10-15 years
(IBEF). Figre 5.9 (a) shows the graph of quarterly Ln_GDP and figure 5.9 (b)
displays the quarterly GDP growth rate.
144
LN_GDP
17.4
17.2
17.0
16.8
16.6
16.4
16.2
16.0
15.8
96
98
00
02
04
06
08
10
12
14
16
Figure 5.9 (a) Quarterly Ln_GDP
G_GDP
6
5
4
3
2
1
0
-1
-2
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.9 (b) Quarterly Growth Rate of Ln_GDP
As shown in the Figure 5.9 (a), the Ln_GDP has been constantly increasing over the
last 20 years. However, the GDP growth rate as shown in figure 5.9(b) has been
volatile over the period 1996 Q3 to 2016Q,4.It can be observed that the period 200809 has been quite low due to the recession. India’s growth over the years has been
145
fuelled by the expansion of services sector. As it can be seen from the graph, the
quarterly GDP growth rate in some quarters was negative. Table 5.4 tabulates the
quarters in the sample period where the GDP growth rate was negative along with the
reasons that can be attributed to the drop in GDP growth rate:
Table 5.4 Reasons for negative GDP growth rate (1996 Q3 – 2016 Q4)
Quarter
GDP growth rate
1996 Q3
-1.19%
Reason
1996-97 Asian crisis
1997 Q2
-0.34%
2000Q4
-0.33%
Kargil war, nuclear blasts resulting in
international sanctions on India
2003 Q4 growth rate was
exceptionally high (4.55). This could
2004 Q1
-1.42%
have an impact on the 2004 Q1
growth rate. However, overall for the
year, the growth rate of GDP was
quite high
2008 Q4
-0.31%
Impact of 2008-09 recession
2009 Q1
-0.81%
Lagged effect of recession in terms
2011 Q3
-0.36%
of high interest rates resulting in
industrial output and slowdown in
construction and mining sector.
In recent years, a higher level of business cycle correlation between developed and
developing economies has been seen due to increasing globalisation. This is contrary
146
to the ‘decoupling theory’ that has been suggested by economists. This theory
suggests that emerging economies will not be affected by the downturn in the
advanced economies due to their substantial foreign exchange reserves, improved
policy framework and relatively healthy banking sector.
CPI
As mentioned earlier, over the last few years, inflation has emerged as a leading
concern for India’s economists and policymakers. The mean quarterly CPI for the
selected period is 87.54 ranging between 42.62 (1996 Q2) to 158.36 (2016 Q4) with
an average growth rate of 1.62% every quarter. Figure 5.10 (a) shows quarterly CPI.
CPI
160
140
120
100
80
60
40
96
98
00
02
04
06
08
10
12
14
16
Figure 5.10 (a) Quarterly CPI
As it can be seen graphically from figure 5.10 (a), CPI has increased over the last few
years. This rising and high inflation persistence has set an intense debate on the nature
of inflation in India and its implications on macroeconomic stability and policy. As
food articles dominate the CPI basket, supply side shocks in the form of episodic food
price increases have been the major cause, leading to higher instances of CPI based
147
inflation. It has been often suggested that in India, higher food inflation is mainly
because of the large increase in the minimum support prices (MSP) policy which
leads to an upwards bias to agricultural prices (Mohanty, 2010). The fuel category
also has some impact on the CPI figures as it comprises of approximately 7% of the
CPI basket as compared to WPI which allocates 14% weightage to fuel and fuel
products. Figure 5.10 (b) displays quarterly growth rate of CPI.
G_CPI
5
4
3
2
1
0
-1
-2
-3
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.10 (b) Quarterly Growth Rate of CPI
The period from 1996Q2 till 1997 Q3, showed a declining trend in the growth rate of
CPI as exhibited in figure 5.10 (b) as a result of structural changes in the
macroeconomic framework. The periods from 1997Q4 to 1998 Q4 and 2008 Q1 to
2013 Q4 (except a few quarters), exhibit a high CPI growth rate.
One of the
important reasons for high CPI was the impact of global recession, which resulted in
the increase in the prices of oil and commodities which in turn, had an adverse impact
on inflation. This also had an impact on the exchange rate. As per empirical evidence,
one percentage point change in the Rupee-dollar exchange rate has 10 basis points on
inflation (Mohanty, 2013). Also, the growth in domestic agricultural production
148
stagnated around 3% per annum, and the demand for food increased which lead to
high food prices thereby affected CPI based inflation.
OIL
The average OIL price for the sample period has been $56.35 per barrel with an
average growth rate of 2.97% per quarter. The maximum OIL price has been recorded
as $ 132.32 per barrel in the quarter 2008 Q2 and the minimum has been recorded as $
9.82 per barrel in 1998 Q4. As discussed in the annual descriptive analysis, price of
OIL depends upon the interplay of demand and supply of oil. Figure 5.11 (a) and (b)
depict the quarterly OIL prices and the growth of OIL prices during the sample
period.
OIL
140
120
100
80
60
40
20
0
96
98
00
02
04
06
08
10
12
14
16
Figure 5.11 (a) Quarterly Brent Oil prices
As it can be seen from figure 5.11 (a) and 5.11 (b) , from 1998 Q1, the oil prices fell
and this was a result of a combination of the Asian Financial crisis of 1997 and an
OPEC miscalculation of world demand that led to a production quota increase in
1997. However, after the massive fall in price, OPEC and non-OPEC countries agreed
149
to a cut the oil production which led to a faster than expected demand recovery in
Asia. After this, the prices continued to rise till 2008 Q2 due to American invasion of
Iraq and rising oil demand in India and China. At the time of financial crisis, the price
of oil decreased significantly due to low demand. However, the prices rebounded
around 2010 Q4 and remained high for the next 4 years. From 2014 Q3 onwards, the
prices again started declining due to oil glut caused by significant oil production in
USA, OPEC and non-OPEC countries (especially Russia). Around this time, USA had
started commercial exploitation of the shale oil reserves using a controversial
technique called fracking. As USA was one of the biggest importers of oil prior to this
period, reduced demand from USA exerted downward pressure on prices. Apart from
that, the demand also reduced from emerging countries, especially China, which
further reduced the prices.
G_OIL
60
40
20
0
-20
-40
-60
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Figure 5.11 (b) Quarterly growth rate of Brent OIL
150
2016
LCU_USD
The mean LCU_USD during the sample period was 48.40. It touched a peak of 67.86
in the quarter 2016 Q4 and the lowest as 34.94 in the quarter 1996 Q2. LCU_USD is
an important variable as the corporate’s debt repayment capacity reduces due to
adverse exchange rate or interest rate shocks and leads to larger than anticipated rise
in new NPA formation. Figure 5.12 (a) exhibits the trends of LCU_USD.
LCU_USD
70
65
60
55
50
45
40
35
30
96
98
00
02
04
06
08
10
12
14
16
Figure 5.12 (a) Quarterly LCU_USD
As it can be observed from the figure, since 1996, after the economy moved towards
market determined exchange rates, four important stages can be seen: i) the Indian
rupee depreciated gradually and steadily against the dollar from 1996 Q2 to 2002 Q4
ii) moderate fluctuations in the exchange rate from 2003 Q1- 2007 Q4 with some
appreciation from 2006 Q3 to 2007 Q4 when rupee appreciated mainly due to the
global weakness of the dollar and capital inflows. iii) sharp depreciation and very
high volatility from 2008 Q1 to 2014 Q3 and iv) greater stability from 2014 Q4 to
2016 Q4. Post 1996, India has seen wide ranging exchange rate reforms and as a
151
result of these reforms and calibrated intervention by RBI over the years, Indian forex
market has increasingly integrated with the global forex markets, especially since
2003-04. Depreciation of rupee normally contributes to higher inflation as it makes
imports more expensive. However, the overall impact depends on the state of the
economy According to Mohanty, 2013, “As per empirical evidence, one percentage
point change in the Rupee-dollar exchange rate has 10 basis points on inflation”.
Figure 5.12 (b) shows the quarterly growth rate of LCU_USD.
G_LCUUSD
12
8
4
0
-4
-8
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.12 (b) Quarterly Growth Rate of LCU_USD
Ln_MCAP NSE
Market capitalisation can be considered as the measure of the corporate size of a
country. In the markets that are emerging, market capitalisation reflects the growth in
the economy. The mean market capitalisation of NSE was Rs. 3,766,778 million for
the quarters 1996 Q2 to 2016 Q4 growing at an average growth rate of 4.89% per
quarter. It ranged between Rs. 391,130 million to Rs. 10,866,060 million. Figure 5.13
152
(a) and figure 5.13 (b) shows the trends of LN_MCAPNSE and growth rate of
Ln_MCAPNSE in the given period.
LN_MCAPNSE
19.0
18.5
18.0
17.5
17.0
16.5
16.0
15.5
15.0
96
98
00
02
04
06
08
10
12
14
16
Figure 5.13 (a) Quarterly Ln_MCAPNSE
G_MCAPNSE
60
50
40
30
20
10
0
-10
-20
-30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.13 (b) Quarterly Growth Rate of MCAPNSE
As it can be seen from the figure, the growth rate of Market Capitalisation of NSE has
been quite volatile. The market capitalisation depends upon a number of domestic and
international factors.
153
SINTT
The mean quarterly SINTT during the sample period is 6.67% and the average
quarterly growth rate is 1.11%. Figure 5.14 (a) shows the trends in the SINTT and
figure 5.14 (b) shows the trends in the growth rate of SINTT. As it can been seen
from figure 5.14 (a), for 4 quarters, from 1997 Q1 and 1997 Q4, the T-bill rates were
low (averaging 5.32%) but they started rising in 1998 Q1. The steep rise in the rates
can be attributed to the combined outflow of funds from the banking system on
account of advance tax payments. The period from 1998 Q1 to 2000 Q4 can be seen
as a period of high yields, with T-bill yields averaging 8.47% (Annual report RBI
June 1998).
SINTT
11
10
9
8
7
6
5
4
3
2
96
98
00
02
04
06
08
10
12
14
16
Figure 5.14 (a) Quarterly SINTT
Post 2001 Q1 till 2008 Q2, the SINTT was near the average of 5.76% with some
volatility. This pre-crisis period was marked by high GDP growth rates and normal
CPI levels. The period of 2008-2010 assumed more significance in the backdrop of
154
the global financial turmoil. In 2008 Q3, the SINTT shot up to 8.94% from 5.75% in
the previous quarter mainly due to the decline in capital inflows, distress in the
economy and reduced liquidity. However, RBI accorded high priority to financial
stability and took several initiatives to improve the efficiency of the money market
and displayed operational readiness for introduction to new products which enabled
the money markets to function normally and sustain adequate money supply in the
system. The mean SINTT in this period (2008 Q4 to 2010 Q3) was 4.07%. Post 2010
Q4 till 2016 Q4, SINTT has been averaging 7.63% within the range of 6% to 9% and
has been quite stable.
G_SINTT
60
40
20
0
-20
-40
-60
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.14 (b) Quarterly Growth Rate of SINTT
LINTT
The average LINTT in the sample period was 8.65% and ranged between 5.14%
(2003 Q4) to 13.96% (1996 Q2). Figure 5.15 (a) and figure 5.15 (b) show the LINTT
rates and growth in LINTT rates respectively during the sample period.
155
As it can be seen, the yields are mostly higher for longer maturities as compared to
shorter maturities. This is primarily due to two reasons a) long term G-secs have
greater duration than short term G-secs. In simple terms ‘Duration’ may be explained
as the length of time that the bond may be affected by an interest rate change and b)
There is a greater probability that interest rates will rise in the longer time period as
compared to shorter time period.
LINTT
16
14
12
10
8
6
4
96
98
00
02
04
06
08
10
12
14
16
Figure 5.15 (a) Quarterly LINTT
In the selected sample, it can be observed that between 1996 Q2 to 2001 Q1, the 10
year G-sec rate remained high with an average of 11.95%, after which it started
falling down and has remained at sub-10% levels. This can mainly be attributed to
repo-cuts around that period, reduction in administered interest rates and expectations
of further reduction in US rates that led to the easing of the liquidity condition and
downward movement in the yields (Report on Currency and Finance, 2007). The
downward trend continued till 2004 Q2 when the yield became as low as 5.87 after
which the G-sec yields again started rising, with an average of around 7.76% from
2004 Q3 to 2016 Q4. 2008 Q4 saw a drastic reduction in G-sec rates (5.30%) due to
156
China slashing interest rates, which spurred expectations of the rate cut in the Indian
bond market, along with low inflation. As graph reflects, during 2013 Q3-2014 Q3,
the G-sec rates remained a little high as compared to previous quarters mainly due to
global reasons, namely, Federal Reserve rate hike scare and volatile capital market
inflows on the global front and low deposit growth, uncertainty around fiscal
consolidation and tight liquidity conditions on the domestic front.
G_LINTT
40
30
20
10
0
-10
-20
-30
-40
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Figure 5.15 (b) Quarterly Growth Rate of LINTT
5.1.4. Stationarity/ Unit Root Test:
The empirical work based on time series models assume that the series is stationary
which implies that the mean and variance of the series is constant over time.
Therefore, checking the variables for Stationarity is the first and one of the most
integral steps in empirical research. The Stationarity of the series is tested by using
the unit root tests. Section 4.4.1 (3) explains the concept of Stationarity. Augmented
Dickey Fuller (ADF) Test has been performed for checking the Stationarity. Further
Phillips-Perron (PP) test have been conducted to increase the validity and reliability of
the ADF test. This section presents the results of the ADF and PP test.
157
Augmented Dickey Fuller (ADF) Test
ADF test for all the variables was conducted as per the automatic selection criterion
(Schwarz Info Criterion) with maximum 11 lags. Schwarz Info Criterion is one of the
most reliable and often used criteria for ADF testing. The reason for using reasonably
high number of lags is to include enough lagged dependent variables to rid the
residuals of serial correlation (Mahadeva & Robinson, 2004).
Table no. 5.5 shows the results of the ADF test. It can be observed from Table 5.5 that
for all the variables, the levels of the series are non-stationary and become stationary
at first difference for Intercept as well as Trend and Intercept. As the data has trend,
the results of no intercept and no trend have not been considered.
Phillips-Perron (PP) Test
To support the results of ADF test, PP test was conducted. For PP test, the Default
(Bartlett Kernel) Spectral estimation method was taken. The selected automatic
bandwidth is Newey-West Bandwidth. Table 5.6 shows the result of the PP test. As it
can be seen, the variables DR and SINTT are integrated of order zero i.e. I(0) and
Ln_GDP, CPI, LCU_USD, OIL, LN_MCAPNSE and LINTT are integrated to first
order i, e. I(1) for both Intercept and Trend and Intercept.
158
Table 5.5 Augmented Dickey Fuller Test (ADF) results
Augmented Dickey-Fuller (ADF)
INTERCEPT
TREND & INTERCEPT
TVARIABLES
DR
LN_GDP
STATISTIC
P-VALUE
STATISTIC
P-VALUE
Level
-2.002
0.286
-2.370
0.392
First Diff
-5.035
0.000
-6.202
0.000
Level
0.909
0.995
-2.737
0.225
-10.057
0.000
-10.054
0.000
Level
1.827
1.000
-0.443
0.984
First Diff
-3.167
0.026
-6.892
0.000
Level
-0.094
0.946
-1.136
0.916
First Diff
-8.523
0.000
-8.528
0.000
Level
-1.768
0.394
-1.906
0.643
First Diff
-8.113
0.000
-8.093
0.000
-0.513
0.883
-2.504
0.325
First Diff
-7.477
0.000
-7.426
0.000
Level
-2.432
0.136
-2.101
0.537
First Diff
-10.445
0.000
-10.604
0.000
Level
-2.521
0.114
-2.518
0.319
First Diff
-13.204
0.000
-13.130
0.000
First Diff
CPI
LCU_USD
OIL
LN_MCAPNSE Level
LINTT
SINTT
T-
159
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
Table 5.6 Phillips- Perron Unit Root Test
PHILLIPS-PERRON UNIT ROOT TEST
INTERCEPT
VARIABLES
T-
TREND & INTERCEPT
P-
T-
STATISTIC VALUE
DR
LN_GDP
CPI
LCU_USD
OIL
SINTT
P-VALUE
Level
-4.12912
0.0015
-4.27517
0.0055
First Diff
-11.923
0.0001 I(0)
-11.8816
0
-2.67852
0.2482
-10.0295
0
-0.57751
0.9776
-6.99048
0
-1.29684
0.8818
-8.52804
0
-1.96276
0.6125
-8.29936
0
-2.40797
0.3728
-7.37522
0
Level
0.909158
0.9952
First Diff
-10.0566
0
Level
3.662587
1
First Diff
-5.69082
0
Level
-0.13104
0.9417
First Diff
-8.52344
0
Level
-1.71094
0.422
First Diff
-8.22491
0
-0.57129
0.8703
First Diff
-7.42928
0
Level
-2.43459
0.1356
-2.02748
0.5776
First Diff
-10.4556
0.0001 I(1)
-10.6135
0
Level
-3.55453
0.0089
-3.53337
0.0424
First Diff
-13.2878
0.0001 I(0)
-13.2146
0
LN_MCAPNSE Level
LINTT
STATISTIC
160
I(1)
I(1)
I(1)
I(1)
I(1)
I(0)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(0
To summarise; ADF and PP test were conducted to examine the stationarity of the
time series data and determine the order of integration of the series. According to
ADF, for all the variables, the levels of the series are non-stationary and become
stationary at first difference for Intercept as well as Trend and Intercept. According to
PP test, the variables DR and SINTT are integrated of order zero i.e. I (0) and
Ln_GDP, CPI, LCU_USD, OIL, LN_MCAPNSE and LINTT are integrated to first
order i,e. I (1) for both Intercept and Trend and Intercept. As PP test relies on
asymptotic theory which suggests that it works well with large samples and our
database is not very large (83 quarters), ADF test results have been taken for further
analysis. According to ADF (Intercept and Intercept and Trend), all the seven series
are integrated of the order 1 i.e. I (1).Therefore, Johansen Cointegration test can be
further conducted.
Figure 5.15 exhibits the graphical representation of Stationarity
30
20
10
0
-10
-20
-30
-40
-50
96
98
00
02
04
D(DR)
D(CPI)
D(OIL)
D(SINTT)
06
08
10
12
14
16
D(LN_GDP)
D(LCU_USD)
D(LN_MCAPNSE)
D(LINTT)
Figure 5.16 Graphical presentation of Stationarity of variables
161
5.1.5. Choice of Lag length
There are no clear guidelines for choosing the correct lag length, especially when in
most likely cases different criteria give contradictory results (Asteriou & Hall, 2006).
However, the lag length should be chosen according to which the residual diagnostics
like Serial correlation and Heteroscedasticity are not present.
Liew suggests that for small samples (t=30 and t=60), AIC and FPE outperform
other criteria as these two have the least probability of underestimation amongst
all criteria compared and these two methods maximise the chance of recovering
the true lag length. Also, for relatively large samples (120 or more observations),
HQC is found to outdo the other techniques in identifying the true lag length (Liew,
2004). Gutierrez et al. (2007) also suggest that Schwarz or Hannan–Quinn criteria for
selection of lag should not be used in case the sample is small due to the tendency of
identifying an under-parameterized model.
Therefore, AIC and FPE can be
considered as ther sample size is around 83 which is less than 120. Ivanov & Kilian
(2005) suggest that for monthly VAR models, AIC tends to produce the most accurate
structural and semi-structural impulse response estimates for realistic sample sizes.
Therefore, in our case, as the sample size is less than 120 and our sample is based on
quarterly data, FPE criteria has been adopted for further analysis.
Table 5.7 shows the details of the lag length selection criteria. As it can be seen,
according to FPE, the desired lag length is 2 and as our data is less than 120 samples,
it has been taken as the lag length selection criteria.
162
Table 5.7 Lag Length Selection Criteria
Endogenous variables: DR LN_GDP CPI LCU_USD OIL LN_MCAPNSE SINTT LINTT
Sample: 1996Q2 2016Q4
Lag
LogL
LR
FPE
AIC
SC
HQ
0
-990.14
NA
35.30098
26.26692
26.51226
26.36496
1
-311.9
1195.847
3.40e-06
10.10266
12.31072*
10.98511*
2
-243.84
105.6734
3.22e-06*
9.995792
14.16658
11.66264
3
-175.97
91.08473*
3.35e-06
9.894027
16.02754
12.34527
4
-104.11
81.31787
3.65e-06
9.687125
17.78336
12.92277
5
-33.831
64.73143
5.25e-06
9.521866
19.58082
13.54191
6
72.73628
75.71879
4.30e-06
8.401677
20.42335
13.20612
7
183.2410
55.25234
6.41e-06
7.177869*
21.16227
12.76671
5.1.6. Johansen Cointegration Test
As all our variables are integrated to the order 1, i.e. I (1), the next step is to check
whether the variables are cointegrated, i.e. whether there is a long run association
between the variables. The concept of cointegration mimics the existence of long-run
equilibrium to which an economic system converges over time. Lag selection is an
important aspect in executing the Johansen Cointegration test.
According to Final Prediction error (FPE) criterion, Lag 2 has been taken. It is also
important to note here that if I(1) variables are modelled jointly in a dynamic system,
163
there can be upto (n-1) cointegrating relationships linking them. (Harris, 1995). Table
5.8 (a) and 5.8 (b) present the results of the Johansen Cointegration test. The result
shows that both Trace statistic and Maximum Eigen statistic are statistically
significant to reject the null hypothesis of r=0, at most 1 and at most 2 cointegrating
equations (CEs) at 5% significance level. Therefore, there are 3 long run
Cointegration relationships between Default rate and the explanatory variables.
Table 5.8 (a) Johansen Cointegration Test – Unrestricted Cointegration Rank
Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.517100
210.6676
159.5297
0.0000
At most 1 *
0.446470
152.4319
125.6154
0.0004
At most 2 *
0.423245
105.1169
95.75366
0.0097
At most 3
0.315424
61.08989
69.81889
0.2034
At most 4
0.151061
30.77346
47.85613
0.6786
At most 5
0.114903
17.67201
29.79707
0.5904
At most 6
0.086464
7.907332
15.49471
0.4754
At most 7
0.008374
0.672776
3.841466
0.4121
Trace test indicates 3 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
- Trace Statistic: As it can be seen from Table 5.8 (a), the p value of the trace
statistic for null hypothesis of no CE(s) is 0.0000 which is less than 5%, hence the
164
null hypothesis of no cointegrating equations is rejected. Also, the p value of the
trace statistic for null hypothesis of ‘At most 1 CE(s)’ and ‘At most 2 CE(s)’ is
0.0004 and .0097 respectively, which again are less than .05, which implies that
the null hypothesis is rejected. Further, the p value for the trace statistic for null
hypothesis ‘At most 3 CE(s)’ is 0.2034 which is more than .05, hence the null
hypothesis cannot be rejected. Therefore, the trace statistic indicates the presence
of 3 cointegrating equations at .05 confidence level.
Table 5.8 (b) Johansen Cointegration Test – Unrestricted Cointegration Rank
Test (Maximum Eigenvalue)
Hypothesized
Max-Eigen
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.517100
58.23562
52.36261
0.0113
At most 1 *
0.446470
47.31509
46.23142
0.0381
At most 2 *
0.423245
44.02696
40.07757
0.0170
At most 3
0.315424
30.31643
33.87687
0.1256
At most 4
0.151061
13.10145
27.58434
0.8793
At most 5
0.114903
9.764683
21.13162
0.7664
At most 6
0.086464
7.234556
14.26460
0.4618
At most 7
0.008374
0.672776
3.841466
0.4121
Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
165
- Eigen Value – Similarly, as exhibited in Table 5.8 (b), the p value of the
maximum eigenvalue statistic for null hypothesis of none CE(s) is 0.0113, which is
less than .05, hence the null hypothesis of no cointegrating equation can be
rejected. Also, the p value of eigen value statistic for null hypothesis of ‘At most 1
CE(s)’ and ‘At most 2 CE(s)’ is 0.0381 and .0170 respectively which again are
less than .05, which implies that the null hypothesis is rejected. Further, the p value
for the eigen value statistic for null hypothesis ‘At most 3 CE(s)’ is 0.1256 which
is more than .05, hence the null hypothesis cannot be rejected. Therefore, the
maximum eigen value statistic also indicates the presence of 3 cointegrating
equations at .05 confidence level.
Therefore, both trace statistic and maximum eigen value statistic in the Johansen
Cointegration test indicates there are 3 cointegrating equations in our model. Hence,
there is a strong evidence of cointegration between DR and the other variables.
5.1.7. Vector Error Correction Model (VECM)
In section 5.1.6, there was a strong evidence of the presence of 3 cointegrating
equations in our model. As it is known, if the variables are cointegrated or have a long
run association, then Vector Error Correction Model (VECM) is employed, but if the
variables are not cointegrated, Vector Auto Regression (VAR) is used. Therefore, in
order to investigate the existence of long run, cointegrating relationship, the study
employs VECM model as given by Johansen (1995).
The vector in our model can be described as
Xt= [DR, Ln_GDP, CPI, LCU_USD, OIL, Ln_McapNSE, SINTT, LINTT]
166
The vector has a VAR representation of the form
=
where
+ ∑
+
Π
is a (nx1) vector of deterministic variables,
is a (nx1) vector of white noise
disturbances, with mean zero and covariance matrix Ξ, and Π is a (n x n) matrix of
coefficients. The above expression may be reparameterised into vector error
correction model (VECM) and can be expressed as:
∆
=
+
Φ ∆X
+Π
+
Where ∆ denotes the first difference operator, Φ is a (n x n) coefficient matrix (equal
to – ∑
Π ), and Π is a (n x n) matrix (equal to ∑
Π − ) whose rank
determines the number of cointegrating vectors. The presence of Cointegration is
indicated by the rank Π (Donald and Ricci, 2003).
In the macroeconomic credit risk model, as all variables were I(1) as per the ADF
method, the next step was to proceed with the lag order selection which was found to
be lag 2 as per FPE selection criterion. Taking lag 2, Johansen Cointegration was
performed which indicated the presence of 3 cointegrating equations. Therefore,
Vector Error Correction Model (VECM) was conducted. Table 5.9 shows the VECM
output with 2 lags and 3 cointegrating equations.
167
Table 5.9 Vector Error Correction Model (VECM) Output
Vector Error Correction Estimates
Sample (adjusted): 1997Q1 2016Q4
Standard errors in ( ) & t-statistics in [ ]
Cointegratin
g Eq:
CointEq1
CointEq2
CointEq3
DR(-1)
1.000000
0.000000
0.000000
LN_GDP(-1) 0.000000
1.000000
0.000000
0.000000
0.000000
1.000000
-0.069885
-0.033651
-6.388084
(0.01610)
(0.00435)
(0.79310)
CPI(-1)
LCU_USD(1)
[-4.34051] [-7.74420] [-8.05457]
OIL(-1)
-0.016959
-0.007955
-1.479778
(0.00470)
(0.00127)
(0.23140)
[-3.61025] [-6.27477] [-6.39498]
LN_MCAPN
SE(-1)
0.162413
-0.155122
5.340545
(0.14331)
(0.03868)
(7.05958)
[ 1.13326]
[-4.01056]
[ 0.75650]
168
SINTT(-1)
0.347960
0.120031
23.27742
(0.07667)
(0.02069)
(3.77658)
[ 4.53857]
[ 5.80101]
[ 6.16362]
LINTT(-1) -0.311854
-0.065914
-16.57960
(0.05597)
(0.01511)
(2.75710)
[-5.57169] [-4.36353] [-6.01342]
C
1.764210
-12.05497
203.4881
Error
D(LCU_USD
Correction:
D(DR)
CointEq1
-0.701951
0.010489
0.860294
(0.18528)
(0.00983)
[-3.78849]
CointEq2
CointEq3
D(DR(-1))
D(LN_GDP) D(CPI)
)
D(LN_MC
D(OIL)
APNSE)
D(SINTT) D(LINTT)
0.720906
-15.47430 -0.328559
-0.974198 -0.608573
(0.81806)
(1.54759)
(10.1310)
(0.95596) (0.47623)
[ 1.06681]
[ 1.05163]
[ 0.46582]
[-1.52743] [-3.05759] [-1.01908] [-1.27791]
-3.868312
0.057591
5.445515
-20.79366
45.19079
3.404119
-10.40758 -9.358462
(1.41146)
(0.07490)
(6.23179)
(11.7892)
(77.1752)
(0.81858)
(7.28229) (3.62778)
[-2.74065]
[ 0.76890]
[ 0.87383] [-1.76379]
0.030328
-0.000532
-0.061816
0.093682
0.250631 -0.012297
0.058440
(0.00815)
(0.00043)
(0.03598)
(0.06806)
(0.44555)
(0.04204) (0.02094)
[ 3.72182]
[-1.22919] [-1.71818]
[ 1.37642]
[ 0.56252] [-2.60214] [ 1.39003] [ 3.00784]
(0.10746)
[ 0.58556] [ 4.15856] [-1.42916] [-2.57967]
(0.00473)
0.062997
0.024902
-0.005560
-0.185165
0.000167
0.006734
0.227187
0.434425 -0.171920
(0.15653)
(0.00831)
(0.69110)
(1.30740)
(8.55862)
(0.09078)
(0.80760) (0.40232)
[ 0.15909]
[-0.66932] [-0.26793]
[ 0.00013]
[ 0.00079] [ 2.50263]
[ 0.53792] [-0.42733]
169
D(DR(-2))
0.118287
0.004302
0.935057
-0.997240
3.465050
0.143165
1.091497
0.336056
(0.12196)
(0.00647)
(0.53847)
(1.01867)
(6.66851)
(0.07073)
(0.62924) (0.31347)
[ 0.96988]
[ 0.66470]
[ 1.73650] [-0.97896]
[ 0.51961] [ 2.02406]
[ 1.73462] [ 1.07206]
2.771715
-0.205982
13.87013
-11.31957
55.97795
2.218076
-3.356446
(2.34381)
(0.12438)
(10.3483)
(19.5767)
(128.154)
(1.35931)
(12.0927) (6.02416)
[ 1.18257]
[-1.65612]
[ 1.34033] [-0.57822]
[ 0.43680] [ 1.63177] [-0.27756] [ 2.96735]
-1.086663
-0.325313
-8.499792
5.294359
-70.38112
1.128874
8.215222
(2.34495)
(0.12444)
(10.3533)
(19.5862)
(128.216)
(1.35996)
(12.0986) (6.02708)
[-0.46341] [-2.61428] [-0.82097]
[ 0.27031]
[-0.54892] [ 0.83008]
[ 0.67902] [ 1.90141]
D(LN_GDP(1))
17.87583
D(LN_GDP(2))
D(CPI(-1))
11.45992
0.056238
-0.000541
0.037262
0.267104
2.203838 -0.012323
-0.074130
(0.03125)
(0.00166)
(0.13797)
(0.26102)
(1.70870)
(0.16123) (0.08032)
[ 1.79958]
[-0.32621]
[ 0.27006]
[ 1.02332]
[ 1.28978] [-0.67995] [-0.45977] [ 0.18630]
D(CPI(-2)) -0.041664
-8.05E-05
0.000261
-0.128412
4.843407
0.009296
0.000724
(0.03343)
(0.00177)
(0.14760)
(0.27923)
(1.82794)
(0.01939)
(0.17249) (0.08593)
[ 2.64965] [ 0.47944]
[ 0.00420] [ 2.60191]
[-1.24627] [-0.04539]
[ 0.00177] [-0.45987]
(0.01812)
0.014964
0.223572
D(LCU_USD
(-1))
-0.010789
-0.001274
0.010250
-0.042457
1.715631
0.015156
0.106012
(0.01944)
(0.00103)
(0.08582)
(0.16235)
(1.06278)
(0.01127)
(0.10028) (0.04996)
[ 1.61428] [ 1.34452]
[ 1.05711] [ 3.08004]
-1.544026 -0.020065
-0.115965 -0.014817
[-0.55505] [-1.23506]
D(LCU_USD 0.033160
-0.003554
[ 0.11943] [-0.26151]
0.048715
0.206884
170
0.153873
(-2))
(0.01951)
(0.00104)
(0.08613)
(0.16294)
(1.06666)
(0.01131)
(0.10065) (0.05014)
[ 1.69979]
[-3.43335]
[ 0.56559]
[ 1.26968]
[-1.44754] [-1.77348] [-1.15216] [-0.29550]
D(OIL(-1)) -0.003740
-4.92E-05
-0.008111
0.014923
0.353370
0.000665
0.037121
(0.00279)
(0.00015)
(0.01232)
(0.02331)
(0.15260)
(0.00162)
(0.01440) (0.00717)
[-1.34000] [-0.33228] [-0.65825]
[ 0.64017]
[ 2.31567] [ 0.41100]
[ 2.57800] [ 2.87657]
0.020634
D(OIL(-2)) -0.000867
-0.000545
-0.016931
0.032635
-0.287541 -0.005542
0.002418 -0.017390
(0.00286)
(0.00015)
(0.01261)
(0.02385)
(0.15611)
(0.01473) (0.00734)
[-0.30378] [-3.59527] [-1.34315]
[ 1.36850]
[-1.84193] [-3.34718] [ 0.16417] [-2.36981]
(0.00166)
D(LN_MCA
PNSE(-1)) -0.484023
0.018260
0.593651
-3.764500
6.650224
0.286408
-1.040989 -1.408325
(0.24349)
(0.01292)
(1.07506)
(2.03379)
(13.3137)
(0.14122)
(1.25629) (0.62584)
[-1.98782]
[ 1.41317]
[ 0.55220] [-1.85098]
PNSE(-2)) -0.240622
0.023155
1.859152
-2.665127
28.28471
0.127601
-1.284267 -0.003196
(0.24243)
(0.01286)
(1.07038)
(2.02492)
(13.2557)
(0.14060)
(1.25081) (0.62311)
[-0.99253]
[ 1.79989]
[ 1.73691] [-1.31616]
0.022229
0.002432
0.267212
-0.116997
-3.602694 -0.023418
-0.371512 -0.201597
(0.03669)
(0.00195)
(0.16200)
(0.30648)
(2.00627)
(0.18931) (0.09431)
[ 0.60581]
[ 1.24900]
[ 1.64942] [-0.38175]
0.011137
0.001551
[ 0.49950] [ 2.02817] [-0.82862] [-2.25030]
D(LN_MCA
[ 2.13378] [ 0.90754] [-1.02675] [-0.00513]
D(SINTT(1))
(0.02128)
[-1.79572] [-1.10046] [-1.96243] [-2.13763]
D(SINTT(2))
0.167006
-0.065851
171
-0.866441 -0.027772
0.014818 -0.008275
(0.02982)
(0.00158)
(0.13164)
(0.24903)
(1.63022)
(0.01729)
(0.15383) (0.07663)
[ 0.37354]
[ 0.98024]
[ 1.26867] [-0.26443]
0.012511
0.006767
-0.252935
-0.867365
1.827479
0.060860
-0.009196 -0.106368
(0.05400)
(0.00287)
(0.23840)
(0.45101)
(2.95241)
(0.03132)
(0.27859) (0.13878)
[ 0.23170]
[ 2.36161] [-1.06095] [-1.92317]
[-0.53149] [-1.60613] [ 0.09633] [-0.10798]
D(LINTT(1))
[ 0.61898] [ 1.94344] [-0.03301] [-0.76642]
D(LINTT(2))
C
-0.054286
0.000235
-0.154825
0.191501
-0.124223
0.061276
0.088559
0.144200
(0.05411)
(0.00287)
(0.23891)
(0.45196)
(2.95866)
(0.03138)
(0.27918) (0.13908)
[-1.00323]
[ 0.08197] [-0.64805]
[ 0.42371]
[-0.04199] [ 1.95260]
[ 0.31721] [ 1.03683]
-0.030066
0.027470
1.142936
0.410301
-10.86454 -0.013509
0.087693 -0.914333
(0.09437)
(0.00501)
(0.41664)
(0.78820)
(5.15975)
(0.48688) (0.24254)
[-0.31861]
[ 5.48570]
[ 2.74321]
[ 0.52056]
[-2.10563] [-0.24683] [ 0.18011] [-3.76975]
0.524634
0.429364
0.514301
0.274135
0.404762
0.521734
0.481899
0.517458
0.374102
0.248662
0.360497
0.044277
0.216269
0.370284
0.317834
0.364654
2.313910
0.006516
45.10631
161.4285
6917.786
0.778277
61.59526
15.28600
equation
0.196380
0.010421
0.867048
1.640267
10.73762
0.113892
1.013207
0.504744
F-statistic
3.485192
2.376095
3.343861
1.192629
2.147365
3.444911
2.937243
3.386401
28.20844
263.1062
-90.59490
-141.5965
-291.9081
71.79290
-103.0574 -47.31150
Akaike AIC -0.205211
-6.077655
2.764872
4.039913
7.797702 -1.294822
3.076435
1.682788
Schwarz SC 0.390296
-5.482148
3.360379
4.635419
8.393208 -0.699316
3.671942
2.278294
R-squared
(0.05473)
Adj. Rsquared
Sum sq.
resids
S.E.
Log
likelihood
172
Mean
dependent
-0.001358
0.016485
1.418274
0.400438
0.369125
0.041054
-0.027022 -0.082254
0.248225
0.012023
1.084230
1.677832
12.12899
0.143522
1.226741
S.D.
dependent
0.633237
As can see from Table 5.9, the R square of the model is 52.46% which is good for a
VECM equation.
As it can be seen from Table 5.9, the target equation of the model is:
D(DR)
=
C(1)*(
DR(-1)
-
0.0698845179145*LCU_USD(-1)
-
0.0169592442316*OIL(-1)+0.162413236316*LN_MCAPNSE(1)+0.347960106381*SINTT(-1)- 0.311853601993*LINTT(-1) + 1.76421018892 ) +
C(2)*( LN_GDP(-1) - 0.0336507892414*LCU_USD(-1) - 0.00795509461783*OIL(1)
-
0.155122264255*LN_MCAPNSE(-1)
0.0659144012451*LINTT(-1)
-
+
0.120030922177*SINTT(-1)
12.0549733955
6.38808374405*LCU_USD(-1)
-
5.34054501542*LN_MCAPNSE(-1)
)
+
C(3)*(
CPI(-1)
1.47977786787*OIL(-1)
+
+
23.2774182552*SINTT(-1)
-
16.5796030691*LINTT(-1) + 203.488091011 ) + C(4)*D(DR(-1)) + C(5)*D(DR(-2))
+
C(6)*D(LN_GDP(-1))
C(9)*D(CPI(-2))
C(12)*D(OIL(-1))
+
+
+
C(7)*D(LN_GDP(-2))
C(10)*D(LCU_USD(-1))
C(13)*D(OIL(-2))
+
C(8)*D(CPI(-1))
+
C(11)*D(LCU_USD(-2))
+
C(14)*D(LN_MCAPNSE(-1))
+
+
+
C(15)*D(LN_MCAPNSE(-2)) + C(16)*D(SINTT(-1)) + C(17)*D(SINTT(-2)) +
C(18)*D(LINTT(-1)) + C(19)*D(LINTT(-2)) + C(20)
And the cointegrating equation can be expressed as:
Ect (t-1) =DR(-1) - 0.0698845179145*LCU_USD(-1) - 0.0169592442316*OIL(-1) +
0.162413236316*LN_MCAPNSE(-1) + 0.347960106381*SINTT(-1) 0.311853601993*LINTT(-1) + 1.76421018892 )
173
In a VAR/ VECM model, as there large number of variables involved, it becomes
difficult to interpret the estimated model, especially when there are lagged variables,
as they may have coefficients which change sign across the lags, thereby making it
difficult to examine the impact of the variables in the system.
In a VECM model, the coefficients may not explain the “sign of causality”, i.e.
whether the endogenous variables affect DR positively or negatively cannot be
inferred through the VECM equation directly. There are a lot of dynamic effects
between the equations that have to be taken into account which can be done through
impulse response function. As explained later in the study, if the IRF is positive for all
periods before stabilising, it can be said that the sign of the causality is positive. If it is
negative, it can be examined it as a negative relationship. However, if the sign of the
variable is positive first and negative later and then it stabilises, it can be inferred that
the sign of the coefficient depends on the time horizon.
While estimating the empirical model using VECM, there can be two different
identification problems. These two issues are -
a)
Identification of the long run structure (i.e. of the cointegrating relations)Long run causality
b)
Identification of the short-run structure (i.e. of the equations of the system)Short run causality
174
5.1.7.1
Long Run Causality
VECM enables us to identify the long run relationships among the endogenous
variables by exploiting the cointegration property and this is in fact one of the most
important reason why it continues to receives the interest of both econometricians and
applied economists. Table 5.10 shows the coefficients of the VECM output which
enable us to understand the long run association between the variables.
Table 5.10 Coefficients of VECM output
Dependent Variable: D(DR)
Coefficient Std. Error t-Statistic
C(1)
-0.701951
0.185285 -3.788494
Prob.
0.0004
C1 is the coefficient of our cointegrated model, also called the ‘error correction term’
or ‘speed of adjustment’ towards long run equilibrium. Our cointegrated model or
error correction term can be expressed as
Ect (t-1) = DR(-1) - 0.0698845179145*LCU_USD(-1) 0.0169592442316*OIL(-1) + 0.162413236316*LN_MCAPNSE(-1) +
0.347960106381*SINTT(-1) - 0.311853601993*LINTT(-1) + 1.76421018892 )
For there to exist a long run association between the variables, C1 must be statistically
significant and the sign must be negative. In the given model case as shown by Table
5.10, coefficient C1 is negative in sign and significant (p<.05). This implies that there
175
is a long run causality running from the explanatory variables Ln_GDP, CPI,
Ln_McapNSE, LCU_USD, OIL,SINTT, LINTT to DR. If the VECM model is
correctly specified, the coefficient C1 will be negative. If it is not negative it implies
that there are instabilities in the model. Negative coefficient means that if there is a
departure in one direction, the correction would have to be pulled back in another
direction to ensure that the equilibrium is retained. In our equation it implies that
70.19 % of departure from equilibrium is corrected in each period i.e. 70.19% of the
disequilibrium is restored/ converge in each quarter. The bigger the (negative)
coefficient, the more rapid is the correction.
Therefore, from the model, it can be inferred that there is a long run causality running
from the explanatory variables Ln_GDP, CPI, Ln_McapNSE, LCU_USD, OIL,
SINTT, LINTT to DR. The reasons for long run causality can be attributed as below:
Ln_ GDP: GDP may have an impact on the DR in the long run. There are different
perspectives to the long run relationship between GDP and default rate. Strong and
sustainable growth in the long run leads to a healthy operating environment and
strong economic growth leads to healthy and profitable asset creation within the
economy which further improves the repayment capacity of the companies, thereby
improving the credit risk position and DR. However, another perspective suggests that
in the long run, such high growth phases generate excess capacity, easy availability of
credit, easier credit monitoring and thus create situations that lead to increased
chances of higher NPAs and accumulation of stressed assets. This has been witnessed
in pre-2008 and post 2008 GDP and DR relationship.
176
CPI: As reflected by the VECM model, CPI also has a long run relationship with DR.
In the long run, CPI erodes the purchasing power of money which leads to a decline
in the disposable income of the people. Directly, it may reduce the repayment
capacity of the borrowers leading to rise in DR. Indirectly, persistent CPI may lead to
lesser disposable income. Apart from this, apprehensions arising from high prices
may lead to less demand by individuals. Moreover, slowdown in investment by
corporates
may affect the entire economic cycle and hence affect the firms’
profitability in the long run. Therefore, this may affect the repayment capacity of both
individual and corporates. Also, the policy measures for reducing inflation have their
externalities and associated costs in terms of reduction in aggregate demand in the
long run (Mohanty, 2010).
Inflation is also important in terms of maintaining the competitiveness of the
domestic industry due to globalisation and market determined exchange rate regime.
With respect to exporters, if there is high inflation, the competitiveness of exporters
will be affected which may lead to reduction in their top line and bottom line, thereby
affected the repayments by such borrowers in the long run. In one of the BIS Central
bankers’ speeches, Dr Deepak Mohanty suggests that if inflation keeps rising and
turns volatile, it raises the inflation risk premia in financial transactions. This, in turn,
pushes up the nominal interest rates, affecting the DR. RBI’s technical assessment
suggests that the threshold level of inflation for India is in the range of 4 to 6% and if
inflation persists beyond this level, it affects economic growth in the long run and
thereby the DR. (Mohanty, 2013).
177
Ln_MCAPNSE: As per the model, there exists a long term relationship between
Ln_MCAPNSE and DR. Stock markets tend to follow the cyclical trends of the
macro economy. When the economy is doing well, it is also reflected in the market
capitalisation and vice-versa. Theory suggests that rising market capital markets lead
to higher returns to the investors thereby lowering the probability of loan defaults.
LCU_USD: With respect to exchange rate changes, as the equation suggests, there is
a long run impact on the DR. This can be understood from two perspectives - those
firms which avail of foreign currency denominated loans, especially when such loans
are not hedged, may suffer from default in case the exchange rate becomes
unfavourable for them in the long run. From the other perspective, the effect of
exchange rate also impacts the exporters and importers differently. An appreciation in
the exchange rate is favourable for importers as they have to pay less local currency,
whereas the depreciation in the currency is favourable for exporters as they receive
more local currency for their exports. Appreciation of domestic currency can
significantly worsen the financial situation of the exporters in the long run which can
have a negative impact on the DR. Hence, this leads to an impact on the default rate.
OIL: The long term impact of Oil prices can again be viewed from two perspectives:
Higher prices of OIL imply an increase in the overall energy costs of the companies
as well as logistic costs, thereby impacting the profits of such concerns, which may
lead to the increase in the defaults. Prices of OIL have an impact on inflation which
further impacts DR.
178
INTEREST RATES- SINTT, LINTT: Interest rates are an important factor in
determining the economic environment of a country. In the long run, interest rates
may have an impact on DR. When interest rates increase, the instalments of loans on
floating rates also increase, and this worsens the financial situation of such debtors.
This may result in the increase in the DR.
-
SINTT has an impact on the interest rates charged by the banks. When SINTT
increases, the rates at which banks borrow from central banks also increase which
reflects in the lending rates of the banks thereby leading to an overall increase in
the lending rate. From the perspective of the borrowers, an increase in SINTT
results in an increase in the cost of borrowing which results in the contraction in
the repayment ability thereby increasing the DR
-
LINTT also impact the lending rates of the banks thereby impacting the DR. In
turn, the yield of 10 year G-sec rates is also influenced by the inflation rate.
When inflation rates are high, the central bank’s priority is to stem the erosion in
purchasing power. This leads to high G-sec yield. In the long run, it is believed
that with the increase in the interest rates, the investment growth reduces which
leads to overall fall in the economic activity which impacts DR.
Normally, the short run interest rates are influenced by near term factors whereas the
long term interest rates are driven by fundamentals. In VECM, an attempt is made to
capture the long run impact through the cointegrating equation. It also enables to
understand the short run impact of endogenous variables through various tests.
179
5.1.7.2
Short Run Causality
In order to check the short run causality between explanatory variables towards the
dependent variable, the WALD statistic is used to check for individual variables. To
support the results of WALD test, the two way causality is checked through Granger
Causality Test. Apart from the above two standard procedures, the Toda-Yamamoto
Test is also conducted with one enhanced lag.
5.1.7.2.1 Wald Test
As discussed above, WALD test shows the joint significance of the lagged impact of
the endogenous variables on the DR. It enables to understand the short run causality
of the given endogenous variables to DR. Sims, Stock and Watson (1990) in their
analysis of causality tests suggest that if the time series display non Stationarity and
are cointegrated, the causality test statistic has a chi square distribution. They also
conclude that the WALD test also has a limiting chi-squared distribution if the time
series are cointegrated.
Therefore, as macroeconomic time series for many countries are non-stationary and
have at least some degree of cointegration, many researchers regard use of WALD
test as encouraging. However, it may involve ‘nuisance parameters’ that are difficult
to observe. Further work has been conducted in this area and researchers recommend
that there are more advanced operational procedures also for testing the causal of one
variable on another group of variables and vice versa (Toda & Phillips, 1991).
Therefore, WALD test is conducted followed by Toda Yamamoto Test which is
considered as an advanced method.
180
The below section discusses the results of the WALD test.
a)
Ln_GDP: In this section, WALD statistic is used to check the short run
causality from ln_GDP to DR The Null hypothesis is:
Null Hypothesis: There is no short run causality running from Ln_GDP to DR.
Table 5.11 shows the results of the WALD test. As it can be seen, the chi square p
value >.05, hence, we fail to reject the Null hypothesis at 5% confidence interval.
This implies there is no short run causality running from Ln_GDP to DR.
Table 5.l1 Wald test for Ln_GDP and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
Df
Probability
F-statistic
1.059996
(2, 60)
0.3529
Chi-square
2.119993
2
0.3465
Value
Std. Err.
C(6)
2.771715
2.343814
C(7)
-1.086663
2.344947
Null Hypothesis: C(6)=C(7)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
Restrictions are linear in coefficients.
Theory suggests that when there is a downturn in the economy and the GDP growth
becomes slow or turns negative, the cash inflows of the firms and households reduce
leading to lessened ability to repay the loan. Moreover, from the perspective of the
181
banks and financial institutions also, they follow stringent credit policy which further
reduces the liquidity in the system thereby leading to increases in the default rate.
Similarly, when the GDP growth rate accelerates, the default rate also declines.
However, this may not happen in the short run, it may take some time for the effects
to be seen. The results support this theory that there is no short run causality running
from LN_GDP to DR. However, there is a long run association between LN_GDP
and DR.
b)
CPI: Next, The Wald test is employed to check whether there is any short run
causality running from CPI to DR. The Null hypothesis is:
Null Hypothesis: There is no short run causality running from CPI to DR
Table 5.12 shows the results of the WALD test. As observed from Table 5.12, chi
square, p value >.05, thus we fail to reject the Null hypothesis implying that there is
no short run causality running from CPI to DR or it can be suggested that there is a
weak causality running from CPI to DR as p value (.0666) is significant at 10%
confidence interval but not significant at 5% confidence interval.
An important point to note here is that CPI is reported with a lag of around 2-3 weeks
currently. In the initial part of the sample, this lag was more which subsumes a lot of
information that would be seen at higher frequencies. This can be one of the causes
for the weak short term relationship between CPI and DR. In the long run, CPI may
contract the purchasing power of firms and individuals and restrict the disposable
income and the thereby the repayment capacity of the borrowers. However, the in the
short run, the effects may not be seen.
182
Table 5.l2 Wald test for CPI and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
Df
Probability
F-statistic
2.709278
(2, 60)
0.0747
Chi-square
5.418556
2
0.0666
Value
Std. Err.
C(8)
0.056238
0.031250
C(9)
-0.041664
0.033431
Null Hypothesis: C(8)=C(9)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
Restrictions are linear in coefficients.
c)
LCU_USD: In the below section, the short run causality running from
LCU_USD to DR is investigated through WALD test. The Null hypothesis is:
Null Hypothesis: There is no short run causality running from LCU_USD to DR.
Table 5.13 exhibits the results of the Wald test. As seen in Table 5.13, chi square
value p>.05, we again fail to reject the Null hypothesis at 5% confidence interval.
This indicates that there is no short run causality running from LCU_USD to DR.
Theory suggests that both appreciation and depreciation of exchange rate may have
an impact on the borrowers. When the exchange rate depreciates, the Rupee becomes
expensive, which affects the importers adversely; similarly when exchange rate
appreciates, it may affect the exporters adversely. Also, if the firms have not hedged
183
their foreign currency denominated loans, their repayment obligations may increase in
the event of depreciation ,which in turn, may affect the credit risk. However, again
this may take some time to reflect in the balance sheets of the corporates and
ultimately affect the DR of the banks in the long run.
Table 5.l3 Wald test for LCU_USD and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
Df
Probability
F-statistic
1.749067
(2, 60)
0.1827
Chi-square
3.498134
2
0.1739
Value
Std. Err.
C(10)
-0.010789
0.019437
C(11)
0.033160
0.019508
Null Hypothesis: C(10)=C(11)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
Restrictions are linear in coefficients.
d)
OIL: Further, Wald test to is employed to check the short run causality
running from OIL to DR. The Null hypothesis is:
Null Hypothesis: There is no short run causality running from OIL to DR.
Table 5.14 displays the result of the WALD test. As seen in Table 5.14, chi square p
value >.05, we again fail to reject the Null hypothesis at 5% confidence interval.
There is no short run causality running from OIL to DR. Low oil prices have
widespread effects on both individual households and corporates.
184
Table 5.l4 Wald test for OIL and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
Df
Probability
F-statistic
0.967817
(2, 60)
0.3858
Chi-square
1.935635
2
0.3799
Value
Std. Err.
C(12)
-0.003740
0.002791
C(13)
-0.000867
0.002855
Null Hypothesis: C(12)=C(13)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
Restrictions are linear in coefficients.
The impact of increase in Oil prices is very significant for a number of reasons. In
India, it is the biggest component of the import bill and its consumption is increasing
rapidly. Rising oil prices pushes up costs both in production and logistics and thereby
leads to price rising, and thus to inflation. In addition, it affects the individuals and
families in terms of rising energy bills and higher cost of living. It also results in the
country’s trade deficit widening and creates pressure on the rupee to depreciate
further, which pushes up the import bills further. However, all these may have an
impact after some time. Hence, it supports the general theory that there is no short run
causality running from OIL to DR but in the long run there is an association between
OIL and DR.
185
e)
LN_MCAPNSE: The current section establishes the presence (or absence) of
short run causality between Ln_MCAPNSE and DR. The Null hypothesis is:
Null Hypothesis: There is no short run causality running from Ln_MCAPNSE to DR.
Table 5.15 shows the results of Wald Test. As it can be seen from Table 5.15, chi
square p value >.05, therefore we fail to reject the Null hypothesis which implies that
there is no short run causality running from Ln_MCAPNSE to DR
Table 5.l5 Wald test for Ln_MCAPNSE and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
Df
Probability
F-statistic
2.088215
(2, 60)
0.1328
Chi-square
4.176430
2
0.1239
Value
Std. Err.
C(14)
-0.484023
0.243494
C(15)
-0.240622
0.242433
Null Hypothesis: C(14)=C(15)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
Restrictions are linear in coefficients.
It is observed that Stock markets tend to follow the cyclical trends of the economy.
Changes in the stock market tend to precede changes in business conditions. From the
individual perspective, it can be observed that when the stock market rise, it delivers
higher returns to the investors and thus it lowers the probability of loan defaults.
Similarly, from the perspective of the companies, rising markets can also be an
186
indication that the companies may be doing fundamentally better. However, the
change may be reflected after a period. Therefore, the results confirm that there is no
short run causality running from Ln_MCAPNSE to DR. However, there is a long run
relationship between Ln_MCAPNSE and DR.
f)
SINTT: Wald test is again conducted to check the short run causality running
from SINTT to DR. The Null hypothesis is:
Null Hypothesis: There is no short run causality running from SINTT to DR.
Table 5.16 exhibits the results of WALD tests for SINTT. As Chi square p value >.05,
we fail to reject the Null hypothesis at 5% confidence interval which implies there is
no short run causality running from SINTT to DR.
Table 5.l6 Wald test for SINTT and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
df
Probability
F-statistic
0.183875
(2, 60)
0.8325
Chi-square
0.367750
2
0.8320
Value
Std. Err.
C(16)
0.022229
0.036693
C(17)
0.011137
0.029815
Null Hypothesis: C(16)=C(17)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
In case of SINTT, it can be said that the short term interest rate (as represented by 14
day T-bill) is very dynamic in nature. Hence there is a possibility that frequent
187
changes in SINTT may not get translated into changes in the lending rates and hence
it may not impact DR in the short run. In the short run, financial institutions may not
be able to pass on the increase in the interest rates to the borrowers. However, the
cointegrating equation suggests that SINTT may have an impact on DR in the long
run. Therefore, it can be concluded there is no short term causality running from
SINTT to DR but in the long run, it may have an impact on the interest rates, and thus
on the Default rate.
g)
LINTT: Similar to other endogenous variables, the short run causality running
from LINTT to DR will be checked in this section. The Null hypothesis is:
Null Hypothesis: There is no short run causality running from LINTT to DR.
Table 5.17 shows the results of the WALD test. As can be observed from Table 5.17,
chi square p value >.05, we fail to reject the Null hypothesis at 5% confidence
interval, which implies that there is no short run causality running from LINTT to DR.
It is normally felt that G-sec yields help in capturing the prevalent interest rate
scenario in the country as lending rates move along the G-sec yields; however it may
not be true always. Due to market dynamics, G-sec yields may incorporate changes
faster in the interest environment as compared to lending rates which may change
slowly due to lags in the transmission as the lending institutions may gradually pass
the reduction in the fall in the interest rates. An economy which has a deeper
corporate bond market may see a faster impact of the changes in the G-sec yields on
the lending rates due to faster market adjustments. However in India, the bond market
188
is yet not fully developed due to which the transmission of changes in interest rates is
slower. Therefore, there may not be a short run causality running from LINTT to DR;
however, as reflected from the VECM model, there is a long run association between
LINTT and DR. To support the results of WALD test, the two way causality of the
model is checked by performing the Granger Causality Test
Table 5.l7 Wald test for LINTT and DR
Wald Test:
Equation: Untitled
Test Statistic
Value
df
Probability
F-statistic
0.584568
(2, 60)
0.5605
Chi-square
1.169136
2
0.5573
Value
Std. Err.
C(18)
0.012511
0.053997
C(19)
-0.054286
0.054111
Null Hypothesis: C(18)=C(19)=0
Null Hypothesis Summary:
Normalized Restriction (= 0)
Restrictions are linear in coefficients.
5.1.7.2.2 Granger Causality Test
The existence of the relationship between two variables does not prove causality or
direction of influence. Granger (1969) has introduced a causality concept which is
based on forecast performance and has received considerable attention in the
theoretical and empirical literature. Lutkepohl has defined Granger causality as “a
variable y2t is causal for a time series variable y1t if the former helps to improve the
forecasts of the latter” (Lutkepohl & Kratzig, 2004). In the given VECM model, it is
189
aimed to perform pairwise granger causality test to determine the direction of
causality of the variables. Table 5.18 presents the statistical results of the VAR
Granger Causality test.
Table 5.18 Results of Granger Causality test
Chi-sq
Prob
D(LN_GDP) granger causes D(DR)
2.120
0.347
D(DR) granger causes D(ln_GDP)
2.220
0.330
D(CPI) granger causes D(DR)
5.419
0.067*
D(DR) granger causes D(CPI)
5.675
0.586
D(LCU_USD) granger causes D(DR)
3.498
0.174
D(DR) granger causes D(LCU_USD)
1.493
0.474
D(OIL) granger causes D(DR)
1.936
0.380
D(DR) granger causes D(OIL)
0.420
0.811
D(Ln_MCAPNSE) granger causes D(DR)
4.176
0.124
D(DR) granger causes D(Ln_Mcapnse)
6.695
0.035**
D(SINTT) granger causes D(DR)
0.368
0.832
D(DR) granger causes D(SINTT)
3.398
0.183
D(LINTT) granger causes D(DR)
1.169
0.557
D(DR) granger causes D(LINTT)
2.928
0.231
**significant at 5% confidence interval
* significant at 10% confidence interval
190
As can be seen from Table 5.18
- There is a unidirectional causality running from D (DR) to D(Ln_MCAPNSE) i.e.
[D(DR)
D(LN_MCAPNSE)].This means that the joint effect of DR(-1) and
DR(-2) on LN_MCAPNSE is significant at 5% confidence interval, based on chi
square statistic of 6.695 and p-value .035. However, the joint effect of
Ln_MCAPNSE(-1) and Ln_MCAPNSE(-2) on DR is not significant with p-value
0.124.
- There is a weak unidirectional causality running from D(CPI) to D(DR) i.e.
[D(CPI)
D(DR)]. This means that the joint effect of CPI(-1) and CPI(-2) on DR
is significant at 10% confidence interval, based on chi square statistic of 5.419 and
p-value .067. However, the joint effect of DR(-1) and DR(-2) on CPI is not
significant with p-value 0.586.
- There is no causality running between DR and joint effects of lag 1 and lag 2 of
Ln_GDP, LCU_USD, OIL, SINTT and LINTT and vice versa.
As it can be seen, the results of the Granger Causality Test support the results of Wald
test. It also enables us to understand the two way causality.
Sometimes, the above results from Wald and Granger Causality test may suffer from
some limitations owing to the sample size. To mitigate these limitations, Toda and
Yamamoto (1995) introduced the Toda-Yamamoto (modified WALD) statistic.
191
5.1.7.2.3 Toda –Yamamoto Causality Test (Modified Wald) Test
Wald tests and Granger causality tests may have non-standard asymptotic properties if
the VAR contains I(1) variables. Also, when the sample size is not large, it may not
satisfy the asymptotics that the cointegration and causality tests rely on. These
problems can be overcome by performing the Toda & Yamamoto tests which over fits
the VAR order (by adding an extra lag) and ignores the extra parameters in testing for
granger causality and enables us to overcome the problems associated with standard
tests, especially the problem of asymptotic properties (Lutkepohl & Kratzig, 2004).
For the test, the model is augmented by adding one more lag, thus although our
WALD test and Granger Causality Test results are based on VECM (2) model , The
Toda –Yamamoto causality test results are based on VECM (3) model. Table 5.19
exhibits the results of the Toda-Yamamoto causality test (modified WALD).
Table 5.19 Toda-Yamamoto Causality (modified WALD) Test Result
Particulars
Chi-sq value
Prob
D(LN_GDP) granger causes D(DR)
3.554
0.169
D(DR) granger causes D(ln_GDP)
2.285
0.319
D(CPI) granger causes D(DR)
6.270
0.044**
D(DR) granger causes D(CPI)
7.277
0.026**
D(LCU_USD) granger causes D(DR)
3.317
0.190
D(DR) granger causes D(LCU_USD)
1.452
0.484
D(OIL) granger causes D(DR)
4.282
0.118
D(DR) granger causes D(OIL)
5.415
0.067*
192
D(Ln_MCAPNSE) granger causes D(DR)
2.841
0.242
D(DR) granger causes D(Ln_Mcapnse)
4.003
0.135
D(SINTT) granger causes D(DR)
1.141
0.565
D(DR) granger causes D(SINTT)
1.354
0.508
D(LINTT) granger causes D(DR)
2.160
0.340
D(DR) granger causes D(LINTT)
6.499
0.039**
**significant at 5% confidence interval
*significant at 10% confidence interval
As it can be seen from the Table 5.19,
- There is a bidirectional causal relationship from D(CPI) to D(DR) and vice
versa i.e. [D(CPI)
D(DR)] and [D(DR)
D(CPI)]at 5% confidence
interval as p value is 0.044 and .026 respectively. The results of the WALD
test show ‘no causality in the short run between CPI and DR’. However, as per
Toda Yamamoto, there is bidirectional causality. It can be explained as
inflation and interest rates affect each other and induce the tendency of the
borrowers to wilfully default in case of inflationary times in the short run.
- There is a unidirectional causal relationship from D(DR) to D(LINTT)
i.e.[D(DR)
D(LINTT)] at 5% confidence interval as p-value is .039.
- There is an evidence of a unidirectional weak causal relationship from D(DR)
to D(OIL). [D(DR)
D(OIL)] at 10% confidence interval as p value is .067.
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SUMMARY OF THE MODEL
1. There is a long run relationship between DR and all the macroeconomic variables,
(Ln_GDP, LCU_USD, OIL, LN_MCAPNSE, SINTT and LINTT) due to the
significant and negative error correction term in VECM model.
2. To investigate the short run causality running from the endogenous variables to
DR, three tests were conducted. WALD test was performed to test the joint
significance of the lagged impact of endogenous variables on DR. The results
suggested a weak causality running from CPI to DR. Pair wise Granger Causality
test was done to substantiate the findings of WALD test and find out the direction
of causality among the variables. The results show a unidirectional causality
running from DR to Ln_MCAPNSE and a weak unidirectional causality running
from CPI to DR.
However, as these two tests may have limitations of non-standard asymptotic
properties and existence of I (1) variables, the Toda Yamamoto test is performed.
The results suggest bidirectional causality running from CPI to DR and vice-versa.
There is a unidirectional causal relationship running from DR to LINTT. There is
also evidence of unidirectional causality relationship between DR and OIL. The
final result can be inferred from the Toda Yamamoto test statistic which implies
that there is a short run causality running from CPI AND LINTT to DR. There is a
weak causality running from OIL to DR. However, there is no short run causality
running from Ln_GDP, LCU_USD, Ln_MCAPNSE and SINTT to DR.
Generally it is assumed that if there is a Cointegrating relationship between two
variables, there must also be Granger causality in at least one direction. However, in
our case, there is no causal relationship between DR and LN_GDP, DR and
194
LCU_USD and DR and LN_MCAPNSE, DR and SINTT. It is important here to
understand that Cointegration analysis and Granger Causality analysis look at data
from different perspectives. The causality tests are based on fairly large models with
many parameters. In our model, the small sample information may make it difficult
for the tests to reject the null hypothesis. Hence, there may be conflict in the results
from the Cointegration analysis and causality tests (Lutkepohl & Kratzig, 2004).
5.1.8. Robustness of the Model: Checking For Residual Diagnostics
It is very important to check whether our model, where DR is the dependent variable,
has any statistical error or not. In this section, an attempt will be made to check the
residuals for Serial correlation and Heteroskadasticity and stability of the model.
5.1.8.1 Serial Correlation / Autocorrelation
One of the important assumptions of our model is that the residuals must not suffer
from serial correlation. Serial correlation or autocorrelation is very likely to occur in
the time-series framework. It is mainly because when the data is arranged in the
chronological order, the error in one period may affect the error in the subsequent
time period (Asteriou & Hall, 2006). Therefore, it can be said that the error term is
said to be serially correlated when error terms from different periods are correlated.
Serial correlation does not affect the unbiasedness or consistency of OLS estimators,
but it affects the accuracy of the estimators, therefore our estimators may not be
BLUE (Best Linear Unbiased Estimators (Asteriou & Hall, 2006).
Also, the
estimated variances of the regression coefficients may be biased and inconsistent
which may make the hypothesis testing invalid. Hence, it is very important to check
the model for serial correlation. Serial correlation may be detected by graphically
195
presentation of the residual plots and statistical tests to check the presence of Serial
correlation. Breusch-Godfrey Serial Correlation LM Test will be performed for
checking the presence of serial correlation.
Breusch-Godfrey Serial Correlation LM Test:
The most frequently used statistical test for the presence of serial correlation is the
Durbin-Watson (DW) test. However, the DW test has some drawbacks which may
make it inappropriate to use in some cases. DW tests may give inconclusive results in
case when the lagged dependent variable is used. Also, it does not take into account
higher orders of serial correlation. Breusch and Godfrey (1978) developed an LM test
in the cases where the Durbin Watson test cannot be applied (Asteriou & Hall, 2006).
The null hypothesis for the test is:
Null Hypothesis: There is no serial correlation in the residuals
Table 5.20 shows the results of the Breusch-Godfrey Serial Correlation LM Test. The
null hypothesis of no serial correlation of the error terms based on Breusch-Godfrey
Serial Correlation LM Test is accepted with a p value >.05.. Therefore, this model is
not suffering from serial correlation.
Table 5.20 Results of Breusch-Godfrey Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
2.049354
Prob. F(2,58)
0.1380
Obs*R-squared
5.280249
Prob. Chi-Square(2)
0.0714
196
5.1.8.2 Heteroscedasticity Test: Breusch-Pagan-Godfrey Test
Another important assumption of the model is that the residuals should have a
constant (equal) variance independent of i which can be presented mathematically as,
var (µi) =σ2
Therefore, having an equal variance means that there is no heteroscedasticity in the
residuals which is an important assumption of the analysis (Asteriou & Hall, 2006).
Heteroscedasticity makes the model inefficient and affects the variances of the
estimated coefficients. It causes the model to underestimate the variances (and
standard
errors)
which
may
make
the
hypothesis
testing
less
reliable.
Heteroscedasticity can be checked graphically by plotting the squared residuals
against the dependent variable and/ or the explanatory variables as well through
statistical testing. For the VECM model, Breusch-Pagan-Godfrey test of
heteroscedasticity can be used.
Breusch-Pagan-Godfrey Test Of Heteroscedasticity
Breusch-Pagan-Godfrey test is a Lagrange multiplier (LM) test for heteroscedasticity.
The test statistic approximately follows a chi-square distribution. The following is the
Null hypothesis:
Null Hypothesis: There is no heteroscedasticity in the residuals
Table 5.21 displays the output for the Breusch-Pagan-Godfrey for Heteroscedasticity.
As it can be observed from Table 5.21, the chi square probability is greater than .05.
Therefore, the null hypothesis cannot be rejected implying that the residuals do not
197
have heteroscedasticity. Hence, it can be concluded that there is no evidence of
heteroscedasticity.
Table 5.21 Results of Breusch-Pagan-Godfrey of Heteroscedasticity Test
Heteroscedasticity Test: Breusch-Pagan-Godfrey
F-statistic
0.771127
Prob. F(24,55)
0.7541
Obs*R-squared
20.14180
Prob. Chi-Square(24)
0.6887
Scaled explained SS
32.75921
Prob. Chi-Square(24)
0.1093
5.1.8.3 Normality Test
Normally, it is assumed that the residuals of the model are independently distributed.
The null Hypothesis in this case is:
Null Hypothesis: The residuals are normally distributed is rejected
20
Series: Residuals
Sample 1997Q1 2016Q4
Observations 80
16
12
8
4
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-8.29e-16
-0.014273
0.702730
-0.481555
0.171143
1.124138
6.782859
Jarque-Bera
Probability
64.54923
0.000000
0
-0.4
-0.2
0.0
0.2
0.4
0.6
Figure 5.17 Results of the normality test for residuals
As it can be observed from figure 5.17, the null hypothesis that the residuals are
normally distributed is rejected (Jarque-Bera p<.05) due to excess kurtosis which
198
implies that data is not normal. However, Paruolo (1997) has demonstrated that in
instances where the normality is rejected due to kurtosis, the results of the Johansen
results will not be affected (Paruolo, 1997; MacDonald & Ricci, 2004; IMF, 2009).
5.1.8.4 Stability Diagnostics CUSUM Test
To check that the model is dynamically stable, CUSUM test is performed. CUSUM
test implies the Cumulative Sum of Recursive Residuals. The CUSUM test was
proposed by Brown, Durbin and Evans (1975). For our model also, the stability of the
functions has been tested using Cumulative sum (CUSUM) test. The test suggests that
so far as the blue trend line is within the red boundary, the model is said to be
dynamically stable. If the CUSUM wanders off too far from the zero line, this is
evidence against the structural stability of the underlying model. (Lutkepohl &
Kratzig, 2004). As it can be observed from figure 5.18, the recursive residuals (blue
line) lie within the red line which implies that the model is stable.
30
20
10
0
-10
-20
-30
02
03
04
05
06
07
08
09
CUSUM
10
11
12
5% Significance
Figure 5.18 Results of CUSUM test
199
13
14
15
16
Figure 5.19 also depicts the stability condition of the model from the perspective of
modulus of roots. The stability conditions state that if the model is stable if all roots
have modulus < 1 and lie inside the unit circle otherwise the results from our Impulse
Response Function and standard errors are invalid. The stability test of ’Inverse roots
of VAR Characteristic Polynomial test’ assess whether all the variables lie inside the
root circle (Banerjee & Murli, 2015). As it can be seen from the figure 5.19 below, the
estimated model is also ‘dynamically stable’.
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Figure 5.19 Stability of the model
5.2.
Macro Stress Testing
Once the final credit risk model has been developed; the presence of a long run
relationship between the endogenous variables has been established and the stability
of the model has been checked, the next step is to subject the model to stress testing.
Using VAR (VECM) to model the dynamics of DR and macroeconomic variables
enables us to carry out Impulse Response Function (IRF) which is the stress test that
200
is proposed in the thesis. Taking IRF as the approach of stress testing has been
adopted by Hoggarth et al, 2005 in which they performs stress testing for UK banks.
In a VAR (VECM) model, as there large number of variables involved, it becomes
difficult to interpret the estimated model, especially when there are lagged variables,
they may have coefficients which change sign across the lags and thereby making it
difficult to examine the impact of the variables in the system. As VAR (VECM)
model captures the interactions between these variables, it allows us for undertaking
the classical impulse response analysis. Therefore, to alleviate this problem, Impulse
Response Function and Variance decomposition can be performed (Quagliariello,
2009; Chris Brooks, 2014).
5.2.1. Impulse Response Function
To examine the banks’ response to shocks, impulse-response functions that are
derived from the VECM model are used. IRFs allow us to trace the dynamic impact
of changes in each of the endogenous variables over time. Therefore, once the
causality is established, the next step is to carry the Impulse Response Function. IRFs
trace out the response of current and future values of each of the variables to a one
unit increase in the current value of one of the VAR errors (Stock & Watson, 2001).
In simple terms ‘Impulse is a one standard deviation shock to error terms’.
The ordering of variables is integral for carrying out Impulse Response Function as
these impulse response functions can be sensitive to the ordering of the variables.
Cholesky ordering is employed to decide the ordering of variables in the VAR system.
Cholesky decomposition enables us to adopt a particular ordering of the variables.
201
Impulse response functions are interpreted under the assumption that ‘all other shocks
are held constant’. However, it is important to note, that when shocks are given to the
variables, such shocks do not occur in each variable one at a time i.e. shocks in these
variables are not independent. Therefore, the error terms may consist of all the
influences that are not directly included in the given variables. Also, correlation of the
error terms may indicate that a shock in one variable is likely to be accompanied by
shock in another variable. Due to this, it is important to orthogonalise the shocks in
the system so that the shocks tracked by IRFs are uncorrelated. There are different
ways to orthogonalise the impulses. These are:
a)
Cholesky decomposition
b)
Spectral decomposition
Sims (1980) suggested that as the residuals are correlated across equations, in order to
see the distinct patterns of movement, it is useful to transform them to orthogonal
form. He further states that there is no unique way to do this. One way is to
triangularise the system. This is referred to as Cholesky decomposition. This
triangularising achieves orthogonalisation but it imposes a recursive structure on the
contemporaneous relationship of the variables. Under this triangularisation, how the
variables are ordered in the model will determine which variable is affected by which.
The variables have been ordered in ascending order according to the likely speed of
reaction to any particular shock. The variables at the front end of the equation are
assumed to affect the following variables contemporaneously. Variables at the bottom
of the VAR, on the other hand, do not affect the preceding variables
contemporaneously but only affect the after a lag. In the words of Sims (1980), an
202
equivalent way to interpret this is that the innovation in the first variable is assumed to
disturb all the variables of the system instantly according to the strength of the
contemporaneous correlation of other residuals with the residual of the first variable,
while the last variable residual is only allowed to affect the last variable in the initial
period.
The ordering adopted in the research was DR LN_GDP CPI LN_MCAPNSE
LCU_USD OIL LINTT SINTT. Interest rates, Oil prices and Exchange rate
(LCU_USD) were at the bottom of the VECM model as the interest rates may react
instantaneously to the shocks. DR, Ln_GDP, CPI and LN_MCAPNSE were kept in
the beginning as they may affect the variables after a lag. In the study, to check the
robustness, different ordering of the variables were considered. However, the results
were very similar, which suggests that our results are not very sensitive to the precise
ordering.
As it can be seen from the graphs also, VECM restricts the long-run behaviour of the
endogenous variables to converge to cointegrating relationships while allowing for
short run adjustment dynamics. The figures show the Impulse response function
projected for the next 20 quarters (5 years). It is important to note that in VECM IRFs,
the standard errors are not reported.
IMPULSE RESPONSE FUNCTION – GRAPHICAL ANALYSIS
Response of DR to DR: Figure 5.20 displays the IRF, when one standard deviation
shock is given to the DR itself. It is observed that with the initial shock, the DR falls
till the 5th quarter, after which it starts increasing till the 11th quarter and ultimately
the DR stabilises over time.
203
Response of DR to DR
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.20 Response of DR to DR
Response of DR to Ln_GDP: Figure 5.21 shows the IRF when one standard
deviation shock is given to the Ln_GDP. The IRF suggests that if one standard
deviation shock is given to Ln_GDP, till the first 4 quarters, the DR falls with the
increase in Ln_GDP after which it starts rising till the 8th quarter. Again, it starts
falling after the 8th quarter till the 13th quarter after which it again starts rising.
However, the quantum of change is very less. Gradually, over time, the innovations
die out and DR almost stabilises. It is also important to note that from quarter 2 to
quarter 5 and again from quarter 11 to quarter 13, the value of DR becomes negative.
The IRF results support the theory that initially with an increase in economic activity,
the DR may fall. However, in the medium to long run, when Ln_GDP rises, which
results in the overall economy improving; this may lead to increased credit off take
and increased capacity build up. However, the increased supply may not correspond
to the demand, which may lead to increased default. This is what happened in the
Indian context also, as explained in the descriptive (annual) section.
204
Response of DR to LN_GDP
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.21Response of DR toLn_GDP
Response of DR to CPI: Figure 5.22 shows the response of DR when one standard
deviation shock is given to CPI.
The response rate to a positive one standard
deviation shock of the CPI in the long run is positive, which implies that in the long
run, with the increase in CPI, the DR is likely to increase. The results support the
previous findings that inflation has both, short run and long run relationship with DR.
As, can be observed from IRF also, an increase in CPI leads to an immediate increase
in the default rate till the second quarter. After the 2nd quarter, for next 2 quarters,
DR starts falling. However, from 5th quarter onwards, the 1 SD shock to CPI leads to
increase in DR. This can be because the purchasing power of the people reduces with
the increase in CPI (inflation), due to which, their ability to repay the borrowed funds
also reduces. Inflation is also important in terms of maintaining the competitiveness
of the domestic industry. With context to exporters, if there is high inflation, the
competitiveness of exporters will be affected, which may lead to reduction in their top
line and bottom line, thereby affected the repayments by such borrowers in the long
run. This may have an adverse impact on the DR. After the twelfth quarter, the
205
forecasted DR stabilises, but remains higher than the normal levels, showing a
tendency of increased DR levels with higher CPI levels.
Response of DR to CPI
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.22 Response of DR to CPI
Response of DR to Ln_MCAPNSE: Figure 5.23 exhibits the IRF of response of DR
to Ln_MCAPNSE. The IRF for a shock on MCAPNSE shows an overall positive
relationship between MCAPNSE and DR. Stock markets tend to follow the cyclical
trends of the macro economy. Theory suggests that rising market capital markets lead
to higher returns to the investors thereby lowering the probability of loan defaults.
However, in the given model, it shows an overall positive relationship i.e. with an
increase in the market cap, the DR increases. However, the effect is not so significant.
In the Indian context, the IRF does not conform to the theory as can be seen from the
figure. As it can be observed in the last few years also, although the stock markets
have been doing quite well, the NPA levels have been increasing due to reasons other
than just the fundamentals of the companies or the stock markets. As can be observed,
the quantum of change is also very less. Till the 2ndquarter, the DR increases with the
206
increase in the market cap, then after a brief fall in the DR for 1 quarter, it again starts
increasing till the 5th quarter after which it again starts falling till the 10th quarterAs
shown in the IRF with a shock to the MCAPNSE, the DR falls sharply in the second
quarter after which it begins to increase marginally till the 6th quarter, after which it
again starts reducing
Response of DR to LN_MCAPNSE
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.23 Response of DR to Ln_MCAPNSE
Response of DR to LCU_USD: Figure 5.24 displays the IRF of a one SD positive
shock to the Exchange rate (LCU_USD) on DR. As it can be observed from the
figure, there is an overall positive effect on the Default rate. When 1 standard
deviation shock is given to the Exchange rate, DR falls minutely in the 2nd quarter
after which it starts increasing till the 3rd quarter. Thereafter, it again starts falling .
By the eleventh quarter, the response stabilises, but remains positive.
207
Response of DR to LCU_USD
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.24 Response of DR to LCU_USD
Response of DR to OIL: Figure 5.25 (a) exhibits the IRF of 1 SD shock to Oil. As it
can be seen from the figure, 1 SD shock to OIL reduces the DR remarkably in the 2nd
quarter, after which it starts increasing. Also, reflected by the Toda Yomamoto test,
there is weak short run causality between OIL and DR.
Theory suggests that an increase in oil prices may lead to overall inflation. Sharp
increases in the oil prices can lead to both negative demand shocks and negative
supply shocks to the economy. It also causes an increase in the energy costs and
thereby, an overall increase in household and business defaults. This can also be seen
in figure 5.25 (b). However, again in our case, there is no significant positive
relationship between OIL prices and DR. In fact, IRF shows a quite stable negative
relationship between OIL and DR and that also is hovering around 0 only. One reason
may be the existence of controlled oil prices in the Indian economy. The oil prices
taken for the analysis is the International Brent crude oil prices. However, in the
Indian context, the oil prices were regulated by the Indian government (Petrol prices
208
till 2010 and diesel prices till 2014) and taxes form a big part of the overall oil prices.
Therefore, it may not be easy to translate the impact of changes in the international oil
prices to DR.
Response of DR to OIL
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
16
18
20
Fig 5.25 (a) Response of DR to OIL
Response of CPI to OIL
4
3
2
1
0
-1
-2
2
4
6
8
10
12
14
Fig 5.25 (b) Response of CPI to OIL
Response of DR to LINTT and SINTT: Figures 5.26 and 5.27 show the response of
DR to LINTT and SINTT respectively. IRF for the response of LINTT shows a
209
positive relationship between DR and LINTT in the long run, which implies that if a
shock is given to LINTT, DR increases i.e. with increase in LINTT, DR also
increases. Initially, the DR falls till the 3rd quarter after which it starts increasing.
After the 15th quarter, the innovations almost die out and the DR stabilises.
Response of DR to LINTT
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.26 Response of DR to LINTT
However, in case of SINTT the impact is opposite in the long run. As it can be seen
there is a negative relationship between SINTT and DR. Till the 2nd quarter, it
increases, after which it starts falling. After the 14th quarter, the innovations almost
stabilise.
210
Response of DR to SINTT
.20
.15
.10
.05
.00
-.05
-.10
2
4
6
8
10
12
14
16
18
20
Fig 5.27 Response of DR to SINTT
Figure 5.28 shows the combined effects of the response of Cholesky one S.D
innovation of all endogenous variables on DR.
Response of DR to Cholesky
One S.D. Innovations
.20
.16
.12
.08
.04
.00
-.04
-.08
2
4
6
8
DR
LN_MCAPNSE
LINTT
10
12
14
LN_GDP
LCU_USD
SINTT
16
18
20
CPI
OIL
Fig 5.28 Response of DR to Cholesky one S.D Innovations
211
Table 5.22 displays the tabular Representation of IRFs for response of DR to
Cholesky one S.D shock to the endogenous variables. The projections have been done
for 20 quarters (5 years).
Table 5.22 Tabular Representation of IRF for Response of DR
Period
DR
LN_GDP
CPI
LN_MCAP
NSE
LCU_USD
OIL
LINTT
SINTT
1
0.196380
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
2
0.079362
-0.00963
0.034056
0.018570
-0.003402
-0.05161
-0.00219
0.018179
3
0.062722
-0.02382
-0.02546
0.014637
0.048995
-0.02787
-0.01497
-0.02482
4
0.025921
-0.02622
-0.01225
0.035400
0.011489
-0.02508
-0.01102
-0.05094
5
-0.03885
-0.0052
0.003922
0.016851
0.012553
-0.00602
0.014646
-0.07382
6
-0.02971
0.004476
0.001723
0.018672
0.013376
-0.00648
0.019613
-0.06467
7
-0.02137
0.012710
0.015814
0.022811
-0.001019
-0.0162
0.034351
-0.05028
8
-6.09E-
0.019454
0.013426
0.008815
0.003229
-0.00879
0.042258
-0.05218
9
0.020086
0.011276
0.016393
0.006584
0.004693
-0.01396
0.036359
-0.04614
10
0.024290
0.002461
0.019781
0.009008
0.007567
-0.01493
0.027573
-0.04858
11
0.030557
-0.00048
0.019348
0.010414
0.012910
-0.00819
0.020158
-0.05693
12
0.025754
-0.0024
0.024513
0.013293
0.014151
-0.00644
0.016961
-0.06332
13
0.018910
-0.00168
0.025831
0.014275
0.016723
-0.00646
0.017094
-0.06777
14
0.017038
0.000520
0.026079
0.014610
0.017342
-0.00651
0.019712
-0.06972
15
0.015674
0.002196
0.027511
0.012534
0.016670
-0.00586
0.023078
-0.0709
16
0.017346
0.002293
0.028282
0.010345
0.016866
-0.00559
0.024178
-0.0707
212
17
0.020481
0.001432
0.029595
0.010033
0.016794
-0.00565
0.023457
-0.06975
18
0.023484
0.000761
0.030690
0.009894
0.017289
-0.0047
0.022285
-0.07006
19
0.025455
-8.34E-06
0.031570
0.010067
0.018070
-0.00418
0.020956
-0.07085
20
0.025652
-0.00067
0.032207
0.010559
0.018814
-0.0043
0.020019
-0.0717
Cholesky Ordering: DR LN_GDP CPI
LN_MCAPNSE LCU_USD OIL SINTT
LINTT
In the VAR model, the IRF goes to 0. However, in the VECM model, the IRFs
stabilise after some point, but do not touch 0.
5.2.2. Variance Decomposition Analysis
The results of VECM indicate the endogeneity and exogeneity of a variable in the
system; however, it does not provide us with the dynamic properties of the system.
The analysis of the dynamic interactions among the variables in the period post
sample
is
conducted
through
Impulse
Response
Function
and
Variance
Decomposition Analysis.
Variance Decomposition is the percentage of the variance of the error made in
forecasting a variable (e.g., Default rate) due to specific shock (e.g. the error term in
the Ln_GDP) at a given time horizon (e.g., 20 quarters). Periods reflect the number of
periods for which forecast is to be done. Variance decomposition function is
performed to identify the contribution of each shock to the changes in the endogenous
variables (Rongjie and Yang, 2011). It gives the proportion of the movements in the
dependent variable that are due to their own shock versus shocks to the other
variables. A shock to a variable will directly affect that variable itself, while
213
transmitting the effect to other variables in the system (Chris Brooks, 2014). As it is
know that in Variance Decomposition Analysis (VDA), the variance of the forecast
errors is decomposed and the percentage of the forecast variance due to each
endogenous variables is determined. Usually, own series of shocks explain most of
the error variance, although the shocks also affect the other variables in the system.
The VDA for VECM model is presented in table 5.23.
The columns show the proportion of the forecast variance for DR from innovations or
shocks to DR, LN_GDP, CPI, LN_MCAPNSE, LCU_USD, OIL, LINTT and SINTT.
Because DR is the first in the variables, the decomposition assumes that the initial
period has all the variance in the forecasts attributed to DR and none to the other
variance i.e. 100% of the variance is explained by DR itself. As the forecast horizon
increases, there is more variation attributed to the other innovations based on the
correlations of the innovations and dynamics of the system. In table, the VDA for 20
quarters i.e. 5 years has been forecasted.
- The VDA of the model specification confirms that the main explanatory power is
attributable to the DR itself (which gradually reduces from 90.69% in the 2nd
quarter to 35.01% in the 20th quarter). Apart from DR itself, the SINTT explains
most of the variations in the DR over the 20 quarter period (which increases from
0.66% in the 2nd quarter to 41.72% in the 20th quarter). Another significant
variable is LINTT which contributes to .01% of the variation in the 2nd quarter and
increases to 6.23% by the 20th quarter followed by CPI which contributes 2.34%
in the second quarter, increasing to 6.47% in the 20th quarter. Therefore, from the
study, it can be implied that interest rates, both short term interest rates and long
run interest rates have a major impact on DR in the long run. In our analysis
214
LN_GDP and LN_MCAPNSE do not contribute much to the variations in the DR.
This does not mean that there is no long run relationship between these variables
and DR, it implies that the contribution to the DR may not be much in the long run.
The results regarding Ln_GDP are in contrast with the studies by Hogarth, 2005
for UK, or Filosa, 2007 for Italian banking. This may be due to different model
specifications or sample periods.
- As it can be seen from the Table 5.23, after 4 quarters or 1 year, about 75.99% of
the forecast variance in DR can be attributed to innovations in DR itself, 2.07% to
Ln_GDP, around 3.01% to CPI, 3.91% to LCU_USD, 2.78% to Ln_Mcapnse,
6.25% to OIL, 5.44%to SINTT and 0.54% to LINTT. As it can be observed, the
majority of the variance in the first year (four quarters) is explained OIL, followed
by SINTT and CPI. This also reinforces our analysis that in the short-run, CPI and
OIL have an impact on DR.
- If variable wise contribution is analysed, both interest rates SINTT and LINTT
contribute to the maximum variance. In case of SINTT, by the 3th quarter, the
contribution increases gradually to 1.61%, after which it increases at an increasing
rate and reaches 5.44% in the 4th quarter and 12.35% in the 5th quarter. It
contributes 41.72% by the 20th quarter (5th year). This may mainly be because the
banks may increase the lending rates in response to increase in t-bill rates which
may translate into default by some borrowers due to inability of borrowers to pay.
However, with the passage of time, the rates in the markets stabilise and thus the
default rate. In case of LINTT, the proportion of variance increases gradually from
.01% in the 2nd quarter. By the 9th quarter it contributes to 5.58% variation in DR
after which for the next 11 quarters, the variation is around 6%. This implies that in
215
the long run, the impact of G-sec stabilises sooner as compared to treasury bills
rates,
- Another variable which contributes to the proportion of the forecast variance for
DR from innovations or shocks is CPI. The contribution of CPI is around 2.32% in
quarter 2 which increases to 6.47% by the end of 20th quarter. This confirms that
in the long run, inflation leads to increase in DR.
- LCU_USD and OIL are other two variables that contribute to the forecast variance.
However, there is an important distinction between the two variables. The
proportion of LCU_USD reaches 4.10% by the third quarter after which it starts
falling till the 11th quarter and reaches 3.03%. After this, it again starts increasing
and reaches 3.44% by the 20th quarter. Contrary to this, the contribution of Oil
increases to 5.38% in the 2nd quarter and gradually decreases to 3.15% by the 20th
quarter. Hence, it substantiates our Toda-Yamamoto results that BRENT has a
short run causality running to DR.
- The contribution of the shocks or innovations of Ln_GDP on DR are not much
(around 1%), which may not be considered very significant.
The VDA substantiates the significant role played by interest rates and CPI in
accounting for fluctuations in DR. That is the reason why Central Banks across the
world increasingly focus on the role of interest rates and CPI (inflation) in the policy
decisions as these impact defaults in a particular banking system and the overall
economic environment. In fact, in the Indian context, with the amendment of RBI Act
in 2016, the “primary objective of the monetary policy is to maintain price stability
while keeping in mind the objective of growth”. For RBI to achieve this mandate, it
must enable monetary transmission to work effectively which consists of typically
216
changing the interest rates by the Central banks. This further is done keeping in view,
its impact on the inflation in the country. Therefore, these two variables have a
significant role.
Table 5.23 Target Model: Variance Decomposition of DR
Peri
od
LN_G
LN_MCA
LCU_U
S.E.
DR
DP
CPI
PNSE
SD
OIL
LINTT
SINTT
1
0.1963
100.000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2
0.2224
90.6854
0.1875
2.3444
0.6970
0.0233
5.3844
0.0096
0.6680
3
0.2425
82.9144
1.1220
3.0718
0.9499
4.0985
5.8458
0.3889
1.6084
4
0.2551
75.9981
2.0707
3.0078
2.7841
3.9083
6.2517
0.5383
5.4406
5
0.2697
70.0386
1.8889
2.7110
2.8799
3.7117
5.6406
0.7761
12.352
6
0.2807
65.7897
1.7696
2.5070
3.1015
3.6542
5.2614
1.2046
16.711
7
0.2901
62.1433
1.8488
2.6444
3.5220
3.4228
5.2380
2.5295
18.650
8
0.2990
58.5071
2.1639
2.6913
3.4028
3.2341
5.0179
4.3785
20.604
9
0.3064
56.1272
2.1953
2.8481
3.2856
3.1023
4.9843
5.5756
21.881
10
0.3136
54.1778
2.1018
3.1164
3.2188
3.0196
4.9844
6.0950
23.285
11
0.3220
52.3103
1.9946
3.3182
3.1590
3.0260
4.7945
6.1755
25.221
12
0.3311
50.0603
1.8910
3.6850
3.1477
3.0435
4.5707
6.1008
27.500
13
0.3407
47.5926
1.7885
4.0553
3.1486
3.1155
4.3532
6.0141
29.931
14
0.3505
45.2046
1.6901
4.3850
3.1487
3.1885
4.1476
5.9987
32.236
15
0.3604
42.9459
1.6023
4.7301
3.0991
3.2297
3.9494
6.0837
34.359
16
0.3701
40.9368
1.5230
5.0683
3.0164
3.2696
3.7673
6.1946
36.223
217
Peri
od
LN_G
LN_MCA
LCU_U
S.E.
DR
DP
CPI
PNSE
SD
OIL
LINTT
SINTT
17
0.3797
39.2042
1.4492
5.4253
2.9371
3.3036
3.6032
6.2700
37.807
18
0.3892
37.6744
1.3795
5.7849
2.8598
3.3414
3.4437
6.2950
39.220
19
0.3987
36.2935
1.3140
6.1370
2.7878
3.3881
3.2912
6.2723
40.515
20
0.4083
35.0076
1.2535
6.4749
2.7256
3.4434
3.1499
6.2222
41.722
Cholesky Ordering: DR LN_GDP CPI LN_MCAPNSE LCU_USD OIL SINTT LINTT
It can be concluded from the VDA that the changes in the interest rates (both short
term and long term) and CPI have relatively important impact on the default rates.
However, Ln_MCAPNSE followed by Ln_GDP have small relative impacts in
predicting the forecast variation in DR.
5.3.
Summary of Findings
The following section summarises the findings of the study based on the research
objectives:
a)
To identify the main macroeconomic variables that affect the credit risk in the Indian
banking and investigate the dynamics between the Financial Soundness Indicator
reflecting credit risk and the identified macroeconomic determinants.
In order to identify the main macroeconomic variables that affect the credit risk in the
Indian banking, a vast array of macro-economic variables which may have an impact
on the credit risk variables were studied extensively over the period of 1996 to 2016
(21 years). In total 19 variables were extensively analysed. The variables were
218
classified as Credit Risk variables and Macroeconomic variables. The credit risk
variables were Default Rate and NPA Ratio. The macroeconomic indicators were
further classified into five categories - Growth/Cyclical indicators (GDP, GDP per
capita, Gross Fixed Capital Formation, Industry Value added), Price stability
indicators (CPI, WPI, M3), External indicators (Exchange rate, Trade, WTI Oil, Brent
Oil), Financial Market indicators (MCAPNSE, MCAPBSE, MCAPWORLD and
MCAPUSA), and Interest rate indicators (Short-term interest-14 day T-bill rate, long
term interest-10 year G-sec rate). Based on the extensive review of literature, detailed
study of the variables and careful examination of their relevance in the Indian
scenario, the final variables that were selected from each category for the model were
Credit Risk (Default rate), Growth indicator (GDP), Price Stability indicator (CPI),
External indicators (Exchange rate and Oil), Financial Market indicator (MCapNSE),
Interest Rate Indicators (Short term interest rate and Long term interest rate). Further,
to investigate the dynamics between the Financial Soundness Indicator reflecting
credit risk (Default rate) and the identified seven macroeconomic determinants, all the
macroeconomic variables were analysed with respect to default rate. Finally, a
parsimonious VECM model was constructed that enabled understanding of the
dynamic long run relationship between these determinants and default risk.
b) To prepare a macroeconomic stress testing model that estimates the relationship
between macroeconomic variables and credit risk.
The macroeconomic stress testing model constructed in the study that estimates the
relationship between macroeconomic variables and credit risk was based on Wislon
model (1997 a & b). It models the default rate of an economic sector to
219
macroeconomic factors. First and foremost, to capture the relationship between credit
risk and the macroeconomic variables 19 macroeconomic indicators were extensively
examined. Based on the extensive literature review and the relevance of each variable
in Indian context, 7 variables were shortlisted. Augmented Dickey Fuller (ADF) test
and Phillips-Perron (PP) Unit root tests were employed to check the Stationarity of
the data. Optimum lag length of 2 was selected using the Final Prediction Error (FPE)
lag length selection criteria.
As the selected variables were I(1), Johansen
Cointegration test was performed to check the presence of cointegrating equations.
Due to the existence of cointegrating equations, a Vector Error Correction Model
(VECM) was constructed to investigate the relationship between Default Rate and the
chosen macroeconomic variables. To investigate the impact of shocks on the default
rate of the banking system, Impulse Response Functions (IRFs) were generated.
Cholesky Decomposition method was used for ordering of the variables for Impulse
Response Functions. Further, to identify the contribution of each shock to the changes
in the Default Rate, Variance Decomposition Analysis was performed. The model
was also checked for Serial correlation, Heteroskadasticity, Normality and Stability
diagnostics.
c)
To evaluate and assess the resilience of the Indian banking system by reviewing the
current macro stress testing methodology for credit risk (conducted by Reserve Bank
of India) with the aim of improving the existing model.
The resilience of the Indian Banking system was evaluated by conducting Macro
Stress tests of the credit risk using the VECM model. Impulse Response Functions
(IRFs) and Variance Decomposition Analysis (VDA) were further employed to
220
investigate the impact of shocks on the default rate. It can be observed from the IRFs,
that after the shocks, the banks stabilise in a short period of time, which reflects the
relative robustness of the banks. The macro stress testing is quite nascent in the Indian
context. Reserve Bank of India started conducting macro stress tests from 2010 before
which micro stress testing tests were only conducted. The methodology has evolved
over the last few years. The objective of the study was to improve the existing model
as adopted by Reserve Bank of India in terms of wider set of endogenous variable
selection, calibration of stress testing scenarios and modification of the macro stress
testing model. In terms of endogenous variable selection, researchers worldwide have
endeavoured to incorporate an array of macroeconomic indicators which may affect
the assessment of credit risk. However, again in the Indian context, very few
macroeconomic variables have been included. In the FSR of December 2016, the
variables include Change in Gross Value Added, Weighted Average Lending Rate,
Exports to GDP ratio, CPI (combined) Inflation and Gross Fiscal Deficit to GDP
ratio. These variables do not capture the entire gamut of dynamics of the financial
system vulnerabilities. There are many variables like money supply, exchange rate,
trade variables, oil prices, financial market variables, unemployment etc. which may
have an impact on the banking system which have not been captured by the Central
Bank’s stress testing exercise. In fact, over the last many years, not many changes
have been introduced to make the model more inclusive. Therefore, there is a need to
re-examine the variables that may have an impact on the stress testing exercise. The
inclusion of many variables may make our credit assessment model quite complicated
and difficult to interpret. Therefore, it is required to assess the relevant factors and
proceed accordingly. The methodology for the same has been discussed in detail in
the research methodology section. With respect to calibration of stress testing
221
scenario, presently, in the methodology adopted by the central bank, the risk scenarios
include a baseline scenario and two adverse macroeconomic scenarios-medium risks
(up to 1 standard deviation) and severe risk (up to 2 standard deviations) based on last
10 years historical data. Although it may be quite inclusive, as the risks are evolving,
there is a need to re-examine the potential scenarios to include a wider range of
possibilities that may not be historical. The recession of 2008 and the dynamic
economic environment thereafter exhibit the need to calibrate the scenarios more
exhaustively. It can be done by employing Impulse Response Function (IRF) and
Variance Decomposition Analysis (VDA) which may capture the dynamic
characteristics and interactions within the empirical model.
The macro stress test model can also be modified as in the Indian context there is a
huge gap in terms of the techniques of modelling currently being used for performing
stress tests vis-a vis the advanced techniques adopted by other countries. Currently,
several time series econometric models are employed by RBI wherein the credit risk
indicator is modelled as a function of macroeconomic variables. In the present thesis,
for investigation into the current position of macro stress testing of credit risk in the
Indian context, time series technique (VECM model along with its variants) has been
employed. The research has further been supplemented by Impulse Response
Function and Variance Decomposition Analysis.
In other countries, various
integrated models are used - Systematic Risk Model (SRM) (Boss et al., 2006), Risk
Assessment Model of Systematic Institutions (RAMSI – Bank of England) (Demekas,
2015, Burrows et al., 2012, Aikman et al., 2009) ; Macro Financial Risk assessment
Framework (MFRAF – Bank of Canada (Gautheir and Souissi, 2012); Correlated
Systematic Liquidity and Solvency Risk (Barnhill and Schumacher, 2011) ; and Stress
222
Test framework with interaction between market and credit risk (Wong and Hui,
2009). There is a huge scope for developing an integrated model, but in the Indian
context, availability of granular data is a major problem. Subject to availability of
data, an integrated model can be developed on the lines of the above models. This
model will have to be adapted in the Indian context as the “one size fits all’ approach
does not suit Stress testing because of differences in financial and macroeconomic
conditions across the globe.
d) To calibrate the most relevant macro stress testing scenarios keeping in view the
existing dynamic vulnerabilities.
As per the macro stress testing practice followed by RBI (FSR, June 2016), the
adverse scenarios are derived based on up to one standard deviation for medium risk
and up to two standard deviations for severe risk (10 years historical data). The
recession of 2007-08 showed that the risks may be unprecedented. Moreover as the
risks are evolving, it is very important here to calibrate risks from a different
perspective.
As the dynamics of the credit risk variables and macroeconomic
variables were modelled through Vector Error Correction Model, it enabled carrying
out of Impulse Response Analysis wherein various shocks to macroeconomic
variables were simulated and projections for the next 20 quarters (5 years) were made.
223
CHAPTER 6
Summary and Conclusion
Global macroeconomic conditions have been highly volatile during the last few years.
In response to this, financial institutions like International Monetary Fund (IMF),
World Bank, Bank for International Settlements (BIS) and Central Banks have been
constantly developing prudential paradigms and sophisticated risk management
techniques to ensure financial stability which has become an important area of
concern. In this context, it is important to develop a comprehensive framework for
financial stability. Important mechanisms for assessing financial stability include
Macro Prudential Analysis and assessment of Systemic Risk. One of the key elements
of Macro Prudential Analysis and Systemic Risk Assessment is Stress Testing, which
is a technique that measures the vulnerability of a portfolio, an institution, or an entire
financial system to rare but plausible shocks under different hypothetical events or
scenarios. Stress Testing was introduced in 1999 as part of Financial Stability
Assessment Programme (FSAP) as a joint initiative of IMF and World Bank. Since
then, IMF and World Bank have emphasized the importance of stress testing with
respect to systemic risk assessment and financial stability modelling.
Theoretically, the resilience of the financial sector can be assessed through a
combination of ‘Micro Stress testing’, which involves periodic assessment of the
financial soundness of individual institutions under adverse economic conditions and
‘Macro Stress testing’, which is aimed at assessing the system wide resilience to
shocks from the macroeconomic environment. Worldwide, the analytical focus of
224
research over the last few years has moved from Micro-Prudential to MacroPrudential dimensions of financial stability However, in the Indian context, there is a
notable absence of research on Macro stress testing; it is one of the modeling areas
which still requires a lot of further research. In this context, there is a need to review
and re-examine the selection of macroeconomic variables and their impact on macro
stress testing along with a reassessment of the technique of risk modelling.
Given this research gap, the main aim of the thesis is to investigate the dynamics
between the Financial Soundness Indicator reflecting credit risk and the
macroeconomic determinants in the Indian banking landscape. An attempt has been
made to identify the main macroeconomic variables that affect credit risk in the
Indian banking sector and further to prepare a macroeconomic stress testing model
that estimates the relationship between macroeconomic variables and credit risk. In
addition, the impact of shocks to these macro variables is studied to evaluate and
assess the resilience of the Indian banking system to credit risk. The current macro
stress testing methodology for credit risk (conducted by Reserve Bank of India) is
reviewed with the aim of improving the existing model. Majority of literature review
has been done from IMF, World Bank, BIS, RBI and Financial Stability Reports of
Central banks across the globe. Other databases used include EBSCO, Proquest,
SSRN and Google Scholar.
The study employs the top-down approach of Macroeconomic Stress testing of Credit
risk in the Indian Banking system by using macroeconomic data and aggregated
Default Rate. Banking system, being the most dominant segment of the Indian
Financial system, is considered as a yardstick to determine whether an economy is
225
strong enough to withstand shocks. And most importantly, in the current landscape of
Indian banking industry, Credit risk is the leading source of risk for banks and it is
very important to identify, measure and control risk and determine the capital
requirement against this risk.
Quarterly data from 1996 (Q2) to 2016 (Q4) (according to calendar year) – 83
quarters pertaining to variables has been collected from Global Economic Monitor
(World Bank) and RBI - Statistical tables related to banking and Handbook of
Statistics on the Indian Economy. Default rate has been taken as a proxy of credit risk.
The main macroeconomic indicators were classified into five categories Growth/Cyclical indicators (GDP, GDP per capita, Gross Fixed Capital Formation,
Industry Value added), Price stability indicators (CPI, WPI, M3), External indicators
(Exchange rate, Trade, WTI Oil, Brent Oil), Financial market indicators (MCAPNSE,
MCAPBSE, MCAPWORLD and MCAPUSA), and Interest rate indicators (Shortterm interest-14 day T-bill rate, long term interest-10 year G-sec rate). Based on an
extensive review of literature and their relevance in the Indian scenario, the final
variables that were selected from each category with the intention of building a
parsimonious model were GDP, CPI, MCapNSE, Exchange rate, Oil, Short term
interest rate and Long term interest rate.
The credit risk model constructed was based on Wilson’s model (1997 a & b) which
models the default rate of an economic sector to macroeconomic factors. Augmented
Dickey Fuller (ADF) test and Phillips-Perron (PP) Unit root tests were employed to
check the Stationarity of the data. Optimum lag length of 2 was selected using the
Final Prediction Error (FPE) lag length selection criteria. As the selected variables
226
were I(1), Johansen Cointegration test was performed to check the presence of
cointegrating equations. Due to the existence of cointegrating equations, a Vector
Error Correction Model (VECM) was constructed to investigate the relationship
between Default Rate and the chosen macroeconomic variables. To investigate the
impact of shocks on the default rate of the banking system, Impulse Response
Functions (IRFs) were generated. Cholesky Decomposition method was used for
ordering of the variables for Impulse Response Functions. Further, to identify the
contribution of each shock to the changes in the Default Rate, Variance
Decomposition Analysis was performed.
The VECM model of Default rate shows a significant relationship between Default
rate and the Macroeconomic variables. The results suggest a long run relationship
running between Default rate and all the variables (GDP, CPI, Exchange rate, Oil,
Market Capitalisation of NSE, Short-term interest rate and Long term interest rate).
Three short-term causality results were performed to establish the short-term
relationships among the endogenous variables. WALD test was performed to test the
joint significance of the lagged impact of endogenous variables on DR. The results
suggested a weak causality running from CPI to DR. Pair wise Granger Causality test
was done to substantiate the findings of WALD test and find out the direction of
causality among the variables. The results show a unidirectional causality running
from DR to Ln_MCAPNSE and a weak unidirectional causality running from CPI to
DR. These two tests may have limitations due to non-standard asymptotic properties.
Further, the existence of I(1) variables necessitates employing the Toda Yamamoto
test with one enhanced lag to mitigate these limitations. The results suggest bidirectional causality running from CPI to DR and vice-versa. There is a uni-
227
directional causal relationship running from DR to LINTT. There is also evidence of
unidirectional causality relationship between DR and OIL. However, the results
should be viewed in light of the fact that the data is quarterly. As the variables are
very dynamic in nature, quarterly data subsumes a lot of information that would be
otherwise seen at higher frequencies. A more robust model can be evolved with
granular data of a higher frequency. The model was also checked for Serial
correlation, Heteroskadasticity, Normality and Stability diagnostics. The BreuschGodfrey Serial Correlation LM Test shows no serial correlation in the residuals of the
model. The Breusch-Pagan-Godfrey test suggests the absence of Heteroscedasticity in
the model, which makes the model more robust. The CUSUM stability diagnostic test
further demonstrates that the model is dynamically stable. The normality assumption
that the residuals are normally distributed is rejected. However, researchers have
demonstrated that in instances where the normality is rejected due to kurtosis, the
results of the Johansen results are not affected. Therefore, it can be stated that the
model is reliable and stable.
IRFs and VDA were further employed to investigate the impact of shocks on the
default rate. Three important results emerge out of the IRFs. With respect to GDP, the
IRFs support the existing theory that initially with an increase in GDP, DR falls.
However, in the long run, an increase in GDP may lead to increased credit off take
and enhanced capacity building which may or may not be complimented with a
corresponding demand and thereby DR increases. With respect to CPI and long term
interest rate; in the long run, DR is likely to increase with increase in CPI and LINTT.
228
The VDA results substantiate the significant role played by interest rates (both short
term interest rates and long term interest rates) and CPI in accounting for fluctuations
in DR in the long run. Apart from these, Exchange rate and Oil also contribute to the
forecast variance of DR in the long run. This also supports the reason why Central
Banks across the world emphasise the role of interest rates and CPI (inflation) in the
policy decisions and why interest rates and inflation should be a matter of concern for
policymakers and government. Although the study confirms a long-run relationship
between GDP and Market cap, its contribution to the variance in DR is found to be
very low. As the banks stabilise in a short period of time, this reflects the relative
robustness of the banks.
LIMITATIONS OF STUDY
a. Availability of data - Data required for stress testing is limited in several ways.
The financial institutions data is often not available (for public use) because of
confidentiality and consistency issues. Insufficient data can lead to non-robust
estimates which may reduce the forecasting ability of our model. In our case also,
the quarterly GDP data is not available prior to 1996 Q2. Also, the NPA data is not
available prior to 1993.
b. Impact of concentration risk: Lately, Indian banking system has been plagued by
large credit exposures to corporates which have become NPAs. It is very difficult
to incorporate the concentration risk in the given model. Also, RBI made it
mandatory for all the banks to disclose concentration risk disclosures from 2010,
therefore data prior to 2010 is not available.
c. Complexity of the models – The lack of granular and consistent data volumes
makes the modelling architecture in this area is very complex and challenging.
229
d. Given the requirement of excessive data volumes and a complex modelling
architecture for the assessment and analysis of an integrated model of systemic
risk, such an analysis may be very challenging without the inputs from the
regulators. Also, the other forms of risk, namely interest rate risk and liquidity risk
have not been accounted for.
e. Endogenity of risk not accounted- Endogenous risk refers to the risk from shocks
that are generated and amplified within the system. On the other hand, exogenous
risks are the shocks that arrive from outside the system (Danielsson and Shin,
2003). In the study, the endogenity of risks has not been accounted for as bank
specific factors and feedback effects have not been incorporated. This provides us
with a further scope for future study.
f. Low-Dimensional VAR/ VECM: As the standard VAR/ VECM model employs
lesser number of variables, it is low dimensional and it might not be able to capture
the full information that may be required by the policy makers and monetary
authorities (Soo, 2012).
CONTRIBUTION OF THE STUDY AND IMPLICATIONS FOR FUTURE
RESEARCH
The study is an attempt to contribute to the ongoing macro prudential research efforts
at both global and domestic level and also facilitate early detection of signals of
financial vulnerabilities. It intends to make an important contribution to the existing
literature in terms of inclusion of more variables that affect the credit risk pertaining
to the banking sector and calibration of stress testing scenarios. It is aimed to take a
wider set of macroeconomic variables and capture the dynamics of the ever changing
financial environment supported with a robust modelling framework involving a
230
variety of econometric techniques. Such an analysis will enable us to have a deeper
understanding of the key determinants of credit and will provide useful information to
explain the resilience of Indian banking system in terms of credit risk.
The results have important implications for the researchers and policy makers.
Banking is a dominant component of the Indian Financial system and is the core of
our macroeconomic policy. Therefore, such a study on the Indian Banking System can
provide useful inputs for regulators and policy makers in enhancing and developing
the existing stress testing framework and make it more inclusive and robust. However,
the banking sector and corporate sector vulnerabilities have risen sharply in the last
few years and thereby present a risk to the financial stability. In the current economic
scenario, the non-performing assets (NPAs) have emerged as one of the most
important sources of risk to the Indian banking sector which has a direct bearing on
the Indian financial landscape and can be considered as a pre-cursor to financial
instability. Recent cases of large scale NPAs have made the banking system very
vulnerable especially a group of a few public sector banks (PSBs) which are highly
vulnerable to further declines in economic conditions. The government is working on
priority on promulgating several frameworks to resolve this problem of NPAs.
The study is an attempt to contribute to the ongoing macro prudential research efforts
and provide a reference point for reassessing and reviewing the mechanism for
checking the resilience of the Indian banking. An important step in this area is to
identify the important and diverse risk variables from a large gamut of
macroeconomic variables which affect the default rate. It is proposed to modify the
existing macro stress testing model for credit risk as developed by Reserve Bank of
231
India in terms of wider set of endogenous variable selection, calibration of stress
testing scenarios and modification of the macro stress testing model. This may be
considered as a contribution towards the improvement and modification of the
existing literature available in the area of macro stress testing of credit risk in the
Indian context. The limitations of the study provide further opportunities for
development of a composite and integrated risk model (as developed by advanced
nations) which aims at examining the interdependencies of the various forms of risk
viz credit risk, market risk and liquidity risks. This study can also provide inputs for
improving the macro stress testing models further by incorporating endogenous
factors as well.
The study is an effort to identify and emphasize on the variables that have a long run
and short run association with default and how the regulators can work on these
variables on priority. This study can provide useful inputs to regulators and policy
makers in enhancing and developing the existing stress testing framework and make it
more inclusive and robust for banks which are a dominant component of the Indian
financial system and core of our macroeconomic policy.
232
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ANNEXURES
Annexure 1: RESULTS OF GRANGER CAUSALITY TEST
VEC Granger Causality/Block Exogeneity Wald Tests
Sample: 1996Q2 2016Q4
Included observations: 80
Dependent variable: D(DR)
Excluded
Chi-sq
df
Prob.
D(LN_GDP)
2.119993
2
0.3465
D(CPI)
5.418556
2
0.0666
D(LCU_USD)
3.498134
2
0.1739
D(OIL)
1.935635
2
0.3799
D(LN_MCAPNSE)
4.176430
2
0.1239
D(SINTT)
0.367750
2
0.8320
D(LINTT)
1.169136
2
0.5573
26.94520
14
0.0196
All
Dependent variable: D(LN_GDP)
Excluded
Chi-sq
df
Prob.
D(DR)
2.215049
2
0.3304
D(CPI)
0.106442
2
0.9482
D(LCU_USD)
12.42767
2
0.0020
D(OIL)
13.19693
2
0.0014
D(LN_MCAPNSE)
4.168384
2
0.1244
D(SINTT)
1.658582
2
0.4364
D(LINTT)
5.675318
2
0.0586
247
All
38.16671
14
0.0005
Dependent variable: D(CPI)
Excluded
Chi-sq
df
Prob.
D(DR)
5.675463
2
0.0586
D(LN_GDP)
3.415051
2
0.1813
D(LCU_USD)
0.322109
2
0.8512
D(OIL)
2.335961
2
0.3110
D(LN_MCAPNSE)
3.024484
2
0.2204
D(SINTT)
2.867225
2
0.2384
D(LINTT)
1.355112
2
0.5079
All
18.90467
14
0.1686
Dependent variable: D(LCU_USD)
Excluded
Chi-sq
df
Prob.
D(DR)
1.492893
2
0.4740
D(LN_GDP)
0.546935
2
0.7607
D(CPI)
1.394008
2
0.4981
D(OIL)
2.380528
2
0.3041
D(LN_MCAPNSE)
4.146417
2
0.1258
D(SINTT)
0.148520
2
0.9284
D(LINTT)
4.264736
2
0.1186
All
17.88612
14
0.2120
Dependent variable: D(OIL)
Excluded
Chi-sq
df
Prob.
D(DR)
0.419765
2
0.8107
248
D(LN_GDP)
0.693543
2
0.7070
D(CPI)
7.969804
2
0.0186
D(LCU_USD)
5.392023
2
0.0675
D(LN_MCAPNSE)
4.559176
2
0.1023
D(SINTT)
3.616793
2
0.1639
D(LINTT)
0.404586
2
0.8169
All
24.25895
14
0.0426
Dependent variable: D(LN_MCAPNSE)
Excluded
Chi-sq
df
Prob.
D(DR)
6.695337
2
0.0352
D(LN_GDP)
2.796375
2
0.2470
D(CPI)
0.783258
2
0.6760
D(LCU_USD)
5.660438
2
0.0590
D(OIL)
11.25964
2
0.0036
D(SINTT)
2.622612
2
0.2695
D(LINTT)
6.511800
2
0.0385
All
35.60485
14
0.0012
Dependent variable: D(SINTT)
Excluded
Chi-sq
df
Prob.
D(DR)
3.398088
2
0.1829
D(LN_GDP)
0.710820
2
0.7009
D(CPI)
0.215065
2
0.8980
D(LCU_USD)
2.804891
2
0.2460
D(OIL)
6.735552
2
0.0345
D(LN_MCAPNSE)
1.382658
2
0.5009
D(LINTT)
0.108141
2
0.9474
249
All
19.55938
14
0.1447
Dependent variable: D(LINTT)
Excluded
Chi-sq
df
Prob.
D(DR)
2.928280
2
0.2313
D(LN_GDP)
9.960898
2
0.0069
D(CPI)
6.787295
2
0.0336
D(LCU_USD)
9.973414
2
0.0068
D(OIL)
13.21525
2
0.0014
D(LN_MCAPNSE)
5.453832
2
0.0654
D(SINTT)
6.500524
2
0.0388
All
49.62890
14
0.0000
250
ANNEXURE 2: RESULTS OF TODA YAMAMOTO TEST
VAR Granger Causality/Block Exogeneity Wald Tests
Sample: 1996Q2 2016Q4
Included observations: 80
Dependent variable: DR
Excluded
Chi-sq
df
Prob.
LN_GDP
3.553860
2
0.1692
CPI
6.269960
2
0.0435
LCU_USD
3.316820
2
0.1904
OIL
4.282396
2
0.1175
SE
2.840921
2
0.2416
SINTT
1.141053
2
0.5652
LINTT
2.159566
2
0.3397
All
23.56805
14
0.0516
LN_MCAPN
Dependent variable: LN_GDP
Excluded
Chi-sq
df
Prob.
DR
2.285161
2
0.3190
CPI
0.194969
2
0.9071
LCU_USD
4.408568
2
0.1103
OIL
5.544129
2
0.0625
SE
0.606303
2
0.7385
SINTT
0.357603
2
0.8363
LINTT
7.209328
2
0.0272
All
32.63234
14
0.0033
LN_MCAPN
251
Dependent variable: CPI
Excluded
Chi-sq
df
Prob.
DR
7.277295
2
0.0263
LN_GDP
4.572451
2
0.1016
LCU_USD
3.194819
2
0.2024
OIL
2.823889
2
0.2437
SE
1.101924
2
0.5764
SINTT
4.511322
2
0.1048
LINTT
0.250260
2
0.8824
All
19.30854
14
0.1535
LN_MCAPN
Dependent variable: LCU_USD
Excluded
Chi-sq
df
Prob.
DR
1.452084
2
0.4838
LN_GDP
2.714528
2
0.2574
CPI
0.966590
2
0.6167
OIL
4.096819
2
0.1289
SE
0.698604
2
0.7052
SINTT
0.317470
2
0.8532
LINTT
9.119165
2
0.0105
All
21.64438
14
0.0862
LN_MCAPN
Dependent variable: OIL
Excluded
Chi-sq
df
Prob.
DR
5.414631
2
0.0667
LN_GDP
0.622407
2
0.7326
CPI
5.437035
2
0.0660
LCU_USD
7.835779
2
0.0199
252
LN_MCAPN
SE
2.199962
2
0.3329
SINTT
7.281633
2
0.0262
LINTT
0.583109
2
0.7471
All
22.55656
14
0.0679
Dependent variable: LN_MCAPNSE
Excluded
Chi-sq
df
Prob.
DR
4.002697
2
0.1352
LN_GDP
17.08795
2
0.0002
CPI
0.935403
2
0.6264
LCU_USD
6.564960
2
0.0375
OIL
18.67461
2
0.0001
SINTT
2.335988
2
0.3110
LINTT
24.13151
2
0.0000
All
51.49628
14
0.0000
Dependent variable: SINTT
Excluded
Chi-sq
df
Prob.
DR
1.353939
2
0.5082
LN_GDP
1.578118
2
0.4543
CPI
0.164525
2
0.9210
LCU_USD
3.258181
2
0.1961
OIL
8.451358
2
0.0146
SE
0.213753
2
0.8986
LINTT
0.422867
2
0.8094
All
20.89613
14
0.1043
LN_MCAPN
Dependent variable: LINTT
253
Excluded
Chi-sq
df
Prob.
DR
6.499181
2
0.0388
LN_GDP
1.558918
2
0.4587
CPI
9.309689
2
0.0095
LCU_USD
6.672718
2
0.0356
OIL
12.98417
2
0.0015
SE
7.014451
2
0.0300
SINTT
4.848798
2
0.0885
All
44.13835
14
0.0001
LN_MCAPN
254
ANNEXURE 3 : IMPULSE RESPONSE FUNCTION
Response to Cholesky One S.D. Innovations
Response of DR to LN_GDP
Response of DR to DR
Response of DR to CPI
Response of DR to LN_MCA PNSE
Response of DR to LCU_USD
Response of DR to OIL
Response of DR to LINTT
Response of DR to S INTT
.2
.2
.2
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.1
.1
.1
.0
.0
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.1
-.1
.0
.0
-.1
5
10
15
20
-.1
5
5
Response of LN_GDP to DR
10
15
10
15
20
5
10
15
20
5
10
15
20
5
10
15
20
5
10
15
20
5
Response of LN_GDP to CPI
Response of LN_GDP to LN_MCAP NSE
Response of LN_GDP to LCU_US D
Response of LN_GDP to OIL
Response of LN_GDP to LINTT
.012
.012
.012
.012
.012
.012
.012
.008
.008
.008
.008
.008
.008
.008
.008
.004
.004
.004
.004
.004
.004
.004
.000
.000
.000
.000
.000
.000
.000
-.004
-.004
-.004
-.004
-.0 04
-.004
-.004
-.008
-.008
-.008
-.008
-.008
-.0 08
-.008
-.008
15
20
5
Response of CP I to DR
10
15
20
5
Response of CPI to LN_GDP
10
15
20
Response of CPI to CPI
5
10
15
20
5
Response of CPI to LN_MCAPNSE
10
15
20
5
Response of CPI to LCU_USD
10
15
20
5
Response of CPI to OIL
10
15
20
5
Response of CPI to LINTT
4
4
4
4
4
4
4
2
2
2
2
2
2
2
2
0
0
0
0
0
0
0
0
-2
-2
-2
-2
-2
-2
-2
-2
5
10
15
20
5
10
15
20
Response of LN_MCAP NSE to LN_GDP
5
10
15
20
Response of LN_MCA PNSE to CP I
5
10
15
20
Response of LN_MCAPNSE to LN_MCAPNSE
5
10
15
20
Response of LN_MCAP NSE to LCU_USD
5
10
15
20
Response of LN_MCAPNSE to OIL
5
10
15
20
Response of LN_MCAPNSE to LINTT
5
.12
.12
.12
.12
.12
.12
.12
.08
.08
.08
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
.00
.00
-.04
-.0 4
-.04
-.0 4
-.04
-.0 4
-.04
-.0 8
-.08
-.0 8
-.08
-.0 8
-.08
-.0 8
-.08
10
15
20
5
10
15
20
5
Response of LCU_USD to LN_GDP
10
15
20
Response of LCU_USD to CPI
5
10
15
20
Response of LCU_USD to LN_MCAPNSE
5
10
15
20
5
Response of LCU_USD to LCU_USD
10
15
20
5
Response of LCU_USD to OIL
10
15
20
5
Response of LCU_USD to LINTT
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
-1
-1
-1
-1
-1
-1
-1
-1
5
10
15
20
5
10
15
20
5
Response of OIL to LN_GDP
10
15
20
Response of OIL to CPI
5
10
15
20
5
Response of OIL to LN_MCA PNSE
10
15
20
5
Response of OIL to LCU_USD
10
15
20
5
Response of OIL to OIL
10
15
20
5
Response of OIL to LINTT
10
10
10
10
10
10
10
5
5
5
5
5
5
5
5
0
0
0
0
0
0
0
0
-5
-5
-5
-5
-5
-5
-5
-5
-10
5
10
15
20
-10
5
Response of LINTT to DR
10
15
20
-1 0
5
Response of LINTT to LN_GDP
10
15
20
Response of LINTT to CPI
-10
5
10
15
20
-1 0
5
Response of LINTT to LN_MCAPNSE
10
15
20
-10
5
Response of LINTT to LCU_USD
10
15
20
10
15
20
5
Response of LINTT to LINTT
.8
.8
.8
.8
.8
.8
.8
.4
.4
.4
.4
.4
.4
.4
.4
.0
.0
.0
.0
.0
.0
.0
.0
-.4
-.4
-.4
-.4
-.4
-.4
-.4
-.4
10
15
20
5
Response of SINTT to DR
10
15
20
5
Response of SINTT to LN_GDP
10
15
20
Response of SINTT to CPI
5
10
15
20
5
Response of SINTT to LN_MCA PNSE
10
15
20
5
Response of SINTT to LCU_US D
10
15
20
5
Response of SINTT to OIL
10
15
20
5
Response of SINTT to LINTT
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
10
15
20
5
10
15
20
5
10
15
20
5
10
255
15
20
5
10
15
20
5
10
15
20
15
20
10
15
20
5
10
15
10
15
20
10
15
20
Response of SINTT to S INTT
1.0
5
10
Response of LINTT to SINTT
.8
5
20
-10
5
Response of LINTT to OIL
15
Response of OIL to S INTT
10
-10
10
Response of LCU_USD to SINTT
2
Response of OIL to DR
20
.04
.00
-.0 4
5
15
Response of LN_MCAPNSE to SINTT
.12
Response of LCU_USD to DR
10
Response of CPI to SINTT
4
Response of LN_MCA PNSE to DR
20
.004
.000
-.004
10
15
Response of LN_GDP to S INTT
.012
5
10
20
Response of LN_GDP to LN_GDP
20
5
10
15
20
ANNEXURE 4: IMPULSE RESPONSE FUNCTION – COMBINED GRAPHS
Response of DR to Cholesky
One S.D. Innovations
Response of LN_GDP to Cholesky
One S.D. Innovations
.20
Response of CPI to Cholesky
One S.D. Innovations
.012
3
.008
2
.08
.004
1
.04
.000
0
-.004
-1
.16
.12
.00
-.04
-.08
-.008
2
4
6
8
10
DR
LN_MCAPNSE
LINT T
12
14
16
LN_GDP
LCU_USD
SINT T
18
-2
2
20
4
CPI
OIL
6
8
10
DR
LN_MCAPNSE
LINT T
Response of LN_MCAPNSE to Cholesky
One S.D. Innovations
12
14
16
LN_GDP
LCU_USD
SINT T
18
2
20
4
CPI
OIL
Response of LCU_USD to Cholesky
One S.D. Innovations
.12
8
10
12
14
16
LN_GDP
LCU_USD
SINT T
18
20
CPI
OIL
Response of OIL to Cholesky
One S.D. Innovations
2.0
12
1.5
.08
6
DR
LN_MCAPNSE
LINT T
8
1.0
.04
4
0.5
.00
0
0.0
-.04
-4
-0.5
-.08
-1.0
2
4
6
8
10
DR
LN_MCAPNSE
LINT T
12
14
16
LN_GDP
LCU_USD
SINT T
18
-8
2
20
4
CPI
OIL
6
8
10
DR
LN_MCAPNSE
LINT T
Response of LINTT to Cholesky
One S.D. Innovations
12
14
16
LN_GDP
LCU_USD
SINT T
18
20
CPI
OIL
1.00
0.75
.4
0.50
.2
0.25
.0
0.00
-.2
-0.25
-.4
-0.50
2
4
6
8
DR
LN_MCAPNSE
LINT T
10
12
14
LN_GDP
LCU_USD
SINT T
16
18
CPI
OIL
20
2
4
6
8
DR
LN_MCAPNSE
LINT T
256
10
12
14
LN_GDP
LCU_USD
SINT T
16
18
CPI
OIL
4
6
8
DR
LN_MCAPNSE
LINT T
Response of SINTT to Cholesky
One S.D. Innovations
.6
2
20
10
12
14
LN_GDP
LCU_USD
SINT T
16
18
CPI
OIL
20
ANNEXURE 5: RESULTS OF VARIANCE DECOMPOSITION ANALYSIS
Variance Decomposition of DR:
Perio
LN_MCAP
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
0.196380
100.0000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
2
0.222422
90.68547
0.187514
2.344429
0.697084
0.023398
5.384436
0.009649
0.668014
3
0.242596
82.91440
1.122028
3.071811
0.949987
4.098518
5.845813
0.388967
1.608473
4
0.255133
75.99812
2.070785
3.007853
2.784133
3.908384
6.251715
0.538394
5.440614
5
0.269789
70.03864
1.888986
2.711068
2.879994
3.711779
5.640685
0.776182
12.35267
6
0.280765
65.78971
1.769603
2.507018
3.101508
3.654233
5.261474
1.204650
16.71181
7
0.290153
62.14333
1.848829
2.644467
3.522088
3.422813
5.238012
2.529517
18.65094
8
0.299033
58.50715
2.163901
2.691321
3.402897
3.234192
5.017947
4.378508
20.60408
9
0.306482
56.12729
2.195349
2.848187
3.285649
3.102342
4.984382
5.575683
21.88111
10
0.313688
54.17783
2.101803
3.116493
3.218890
3.019632
4.984478
6.095086
23.28579
11
0.322022
52.31038
1.994650
3.318256
3.159016
3.026088
4.794524
6.175558
25.22152
12
0.331186
50.06032
1.891026
3.685011
3.147712
3.043512
4.570710
6.100803
27.50091
13
0.340767
47.59266
1.788595
4.055316
3.148680
3.115597
4.353223
6.014184
29.93174
14
0.350570
45.20462
1.690192
4.385095
3.148739
3.188501
4.147675
5.998742
32.23644
15
0.360465
42.94591
1.602380
4.730121
3.099142
3.229722
3.949480
6.083795
34.35945
16
0.370198
40.93687
1.523065
5.068329
3.016413
3.269694
3.767353
6.194648
36.22363
17
0.379702
39.20426
1.449201
5.425318
2.937130
3.303697
3.603296
6.270082
37.80702
18
0.389219
37.67442
1.379575
5.784965
2.859865
3.341411
3.443777
6.295011
39.22097
19
0.398799
36.29350
1.314089
6.137033
2.787830
3.388103
3.291281
6.272332
40.51583
20
0.408365
35.00762
1.253515
6.474906
2.725604
3.443495
3.149993
6.222233
41.72263
d
Variance Decomposition of LN_GDP:
LN_MCAP
Period
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
0.010421
0.632899
99.36710
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
2
0.015511
0.427145
88.30613
0.203109
1.638817
0.893786
2.791007
5.601956
0.138049
3
0.018933
0.486101
82.30333
0.883515
4.721245
2.800322
2.037507
6.668775
0.099207
4
0.023071
0.345376
78.93382
1.004793
5.111180
2.562641
2.463017
9.457104
0.122073
5
0.026713
0.405024
77.06934
1.135496
4.446993
2.312859
1.856244
11.94274
0.831307
257
6
0.029381
0.415943
75.76721
1.188532
4.211191
2.208477
1.567919
13.10800
1.532724
7
0.031818
0.455163
74.75402
1.034790
4.440347
2.167671
1.389255
13.41200
2.346753
8
0.034349
0.400073
74.10154
0.889349
4.485033
1.981445
1.197196
13.59528
3.350087
9
0.036748
0.350475
73.31390
0.788486
4.539433
1.819069
1.048312
13.77865
4.361669
10
0.039005
0.313293
72.56733
0.722622
4.567829
1.662100
0.933885
13.96236
5.270578
11
0.041198
0.282670
71.82926
0.670961
4.535348
1.510633
0.837159
14.12665
6.207320
12
0.043286
0.261556
71.06457
0.654701
4.453795
1.383426
0.762213
14.23872
7.181022
13
0.045253
0.247359
70.33949
0.673736
4.379609
1.273007
0.703077
14.25943
8.124292
14
0.047145
0.245776
69.66728
0.716544
4.337493
1.175772
0.655629
14.20721
8.994294
15
0.049004
0.254384
69.00178
0.776866
4.293812
1.089216
0.619584
14.13505
9.829307
16
0.050812
0.266966
68.35770
0.846526
4.241563
1.013135
0.589436
14.05994
10.62473
17
0.052561
0.280938
67.75098
0.919925
4.192014
0.946897
0.562396
13.98454
11.36231
18
0.054265
0.297717
67.17125
0.994163
4.141402
0.888786
0.541242
13.90666
12.05878
19
0.055927
0.316883
66.61744
1.072195
4.088891
0.837546
0.525586
13.82490
12.71656
20
0.057544
0.336915
66.09745
1.153898
4.040190
0.792362
0.512816
13.73927
13.32710
Variance Decomposition of CPI:
Perio
LN_MCAP
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
0.867048
8.246606
0.508387
91.24501
0.000000
0.000000
0.000000
0.000000
0.000000
2
1.409378
11.10622
0.597962
83.15588
0.303233
0.176139
2.588997
0.003576
2.067997
3
1.998204
17.19090
0.310258
74.09225
0.173545
1.091304
3.464334
0.001847
3.675560
4
2.580705
16.90526
0.226373
71.06750
0.139966
1.457445
3.219709
0.019423
6.964324
5
3.181951
16.56814
0.168623
66.68881
0.284228
2.307713
3.481895
0.016026
10.48456
6
3.807581
15.77520
0.190434
63.63664
0.366849
2.809088
3.896862
0.031484
13.29344
7
4.448154
15.08454
0.231141
61.34561
0.453303
3.210589
4.225423
0.059305
15.39009
8
5.098688
14.85863
0.254914
59.45600
0.500620
3.534534
4.468846
0.093345
16.83311
9
5.746884
14.84637
0.274242
57.95727
0.540045
3.810654
4.639423
0.122005
17.80999
10
6.386371
14.96312
0.302718
56.62065
0.578339
4.072867
4.736727
0.152246
18.57334
11
7.012642
15.10589
0.338499
55.47891
0.610939
4.307034
4.790406
0.186174
19.18215
12
7.626393
15.20263
0.377028
54.47946
0.639519
4.517690
4.846922
0.221996
19.71475
13
8.226208
15.26175
0.415409
53.62052
0.662146
4.700765
4.902846
0.259693
20.17688
14
8.809864
15.28511
0.449764
52.91397
0.678051
4.858246
4.948787
0.295176
20.57090
15
9.376632
15.29689
0.477899
52.31374
0.690890
4.996799
4.989220
0.325780
20.90878
16
9.926432
15.30842
0.501495
51.80112
0.702726
5.117082
5.022079
0.351140
21.19594
17
10.45961
15.32004
0.522435
51.35982
0.714438
5.222151
5.048089
0.372722
21.44030
d
258
18
10.97720
15.33648
0.541620
50.97134
0.725575
5.314809
5.070181
0.392127
21.64788
19
11.48030
15.35524
0.559439
50.62810
0.735518
5.395975
5.089966
0.410192
21.82557
20
11.96956
15.37367
0.575886
50.32338
0.744162
5.468065
5.107739
0.427193
21.97990
Variance Decomposition of LN_MCAPNSE:
Perio
LN_MCAP
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
0.113892
3.186069
25.01180
3.352350
68.44978
0.000000
0.000000
0.000000
0.000000
2
0.179918
7.281810
37.89806
2.599073
41.82348
0.157812
0.089744
9.521376
0.628647
3
0.244785
12.12809
36.38798
3.487741
26.72660
0.147910
1.406560
18.09443
1.620687
4
0.302443
14.54639
34.35192
4.683569
19.44191
0.149406
2.829953
22.91884
1.078010
5
0.342449
14.12572
34.38570
5.471315
16.24126
0.200870
2.505980
26.08032
0.988837
6
0.370306
13.26253
35.26054
5.424782
14.68759
0.351655
2.218229
27.80617
0.988496
7
0.392964
12.58562
36.07618
5.149705
14.26780
0.381358
2.119811
28.46532
0.954213
8
0.414458
11.72886
36.89317
4.999856
14.35736
0.355155
2.050793
28.64571
0.969092
9
0.436511
11.03052
37.46189
4.841498
14.27595
0.324682
1.981042
28.94716
1.137254
10
0.458418
10.72762
37.68653
4.718080
13.96966
0.294539
1.937741
29.26382
1.402008
11
0.479547
10.60118
37.74256
4.618296
13.69006
0.269159
1.903824
29.52230
1.652616
12
0.499890
10.48739
37.84045
4.482245
13.42777
0.248350
1.828863
29.77562
1.909318
13
0.519196
10.32231
38.02566
4.322153
13.17593
0.230955
1.746677
30.03453
2.141786
14
0.537393
10.11751
38.23111
4.162765
12.99938
0.215912
1.689044
30.26697
2.317302
15
0.554776
9.876809
38.43621
4.023371
12.86479
0.202596
1.643938
30.48055
2.471736
16
0.571440
9.635592
38.61785
3.901607
12.73745
0.191110
1.601040
30.67950
2.635852
17
0.587413
9.433763
38.75516
3.790538
12.62350
0.181412
1.563240
30.84576
2.806636
18
0.602848
9.265137
38.86263
3.688169
12.53213
0.173283
1.527724
30.97242
2.978503
19
0.617928
9.115129
38.95957
3.589878
12.45335
0.166135
1.490993
31.07535
3.149586
20
0.632720
8.980263
39.05011
3.494468
12.37677
0.159744
1.455339
31.16932
3.313988
d
Variance Decomposition of LCU_USD:
Perio
d
LN_MCAP
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
1.640267
2.775791
1.673041
5.150670
9.216405
81.18409
0.000000
0.000000
0.000000
2
2.484531
6.012372
6.784833
7.136806
9.361797
64.96601
0.007005
5.273703
0.457475
3
3.212834
6.734824
8.542507
9.080024
8.932531
59.75651
0.228328
6.302776
0.422497
4
3.946750
6.886205
9.658447
9.602178
7.508789
55.60410
1.008177
8.972764
0.759342
5
4.569643
7.076289
9.998616
10.61704
6.534019
53.57635
0.997292
10.45539
0.745016
259
6
5.084530
6.702261
10.01459
11.25964
5.963599
53.23083
0.992173
11.01843
0.818478
7
5.542165
6.550618
9.949391
11.24482
5.802467
53.48809
0.988461
11.15262
0.823533
8
5.949608
6.314234
10.07948
11.37819
5.774620
53.49636
0.944636
11.19876
0.813709
9
6.333647
6.147423
10.17320
11.38391
5.850012
53.45810
0.938429
11.26796
0.780968
10
6.700640
6.124053
10.27619
11.40982
5.854297
53.25460
0.946756
11.39969
0.734601
11
7.049279
6.122252
10.36539
11.46767
5.830434
53.01166
0.951368
11.56266
0.688567
12
7.382207
6.146017
10.42365
11.47225
5.792815
52.86306
0.946626
11.70882
0.646757
13
7.696979
6.148760
10.48420
11.45605
5.754046
52.77637
0.934776
11.83553
0.610258
14
7.996094
6.120457
10.54331
11.41721
5.735378
52.74491
0.923922
11.93413
0.580679
15
8.281713
6.077150
10.60208
11.37426
5.726502
52.72860
0.914956
12.02159
0.554862
16
8.555406
6.027843
10.65552
11.34034
5.720995
52.71628
0.908000
12.10013
0.530895
17
8.819069
5.986693
10.69872
11.31021
5.716664
52.70631
0.902789
12.17017
0.508437
18
9.073788
5.955098
10.73465
11.28391
5.712663
52.69795
0.896987
12.23168
0.487061
19
9.320728
5.929487
10.76667
11.25631
5.709766
52.69442
0.890389
12.28589
0.467069
20
9.560804
5.908091
10.79721
11.22558
5.707712
52.69369
0.883594
12.33551
0.448604
Variance Decomposition of OIL:
LN_MCAPN
Period
S.E.
DR
LN_GDP
CPI
SE
LCU_USD
OIL
LINTT
SINTT
1
10.73762
0.361962
2.850580
14.08149
1.731072
20.35997
60.61493
0.000000
0.000000
2
15.67317
4.294711
4.816382
19.65353
1.472091
16.80279
51.11162
0.045857
1.803017
3
19.21716
5.178295
7.164352
20.38074
4.710297
17.93264
37.44340
0.187662
7.002608
4
22.75848
5.173492
11.74579
16.85877
4.873332
18.80646
29.27828
2.899961
10.36391
5
25.68990
5.378738
13.78884
14.16091
3.921151
19.84406
25.74375
5.791053
11.37151
6
28.21176
6.075598
13.67768
12.59293
3.288831
21.45226
22.70519
6.778086
13.42943
7
30.53106
6.292604
13.43984
11.50529
2.950610
22.69934
21.05532
6.894540
15.16246
8
32.77717
6.017141
13.42170
10.76433
2.740123
23.68259
20.47136
6.760000
16.14276
9
34.91417
5.943140
13.41602
10.34342
2.628851
24.41911
19.62663
6.641054
16.98179
10
36.89529
6.010089
13.51629
9.851915
2.595248
24.86385
18.66721
6.619072
17.87633
11
38.79663
6.084973
13.67306
9.317914
2.543705
25.23727
17.87614
6.738144
18.52881
12
40.64250
6.258069
13.76087
8.846101
2.439151
25.60413
17.25074
6.895388
18.94555
13
42.41970
6.447130
13.77050
8.448253
2.345027
25.92925
16.69772
6.983450
19.37867
14
44.15173
6.560717
13.76834
8.110696
2.283848
26.22248
16.22594
7.017576
19.81040
15
45.83511
6.608835
13.79538
7.821481
2.233134
26.47581
15.84296
7.046684
20.17571
16
47.45821
6.645177
13.82411
7.569425
2.189985
26.69246
15.48109
7.076328
20.52142
17
49.02214
6.688346
13.84522
7.336132
2.156126
26.87782
15.13493
7.105859
20.85557
260
18
50.53565
6.732440
13.86491
7.118112
2.124963
27.04489
14.83552
7.137635
21.14152
19
52.00912
6.784505
13.87718
6.921935
2.093927
27.19936
14.57572
7.165865
21.38150
20
53.44694
6.841698
13.88278
6.746101
2.066864
27.33767
14.33911
7.186074
21.59971
Variance Decomposition of LINTT:
LN_MCAP
Period
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
0.504744
0.178583
0.948508
12.03652
3.326165
0.518885
15.69093
67.30041
0.000000
2
0.692911
4.008246
3.336127
20.03227
2.251004
1.065107
19.16824
49.25609
0.882918
3
0.872662
2.547965
6.838649
25.99471
5.910764
1.955026
12.23499
42.93040
1.587493
4
1.060554
2.236578
9.694507
24.37869
11.16339
1.587870
8.909671
40.95434
1.074954
5
1.227653
1.823252
12.38991
21.09090
11.43104
2.005100
7.141794
42.93462
1.183387
6
1.392339
2.804111
12.92531
19.76546
11.70549
1.941018
5.570258
43.67099
1.617363
7
1.548687
3.791472
13.43727
18.22625
11.54104
2.086725
4.634074
44.09953
2.183642
8
1.700457
4.000436
13.76539
17.50474
11.48759
2.074408
4.154622
44.01496
2.997860
9
1.851029
4.159808
13.99674
17.54218
11.20519
2.020645
3.773210
43.61269
3.689550
10
1.991781
4.036487
14.21116
17.66386
11.04632
2.094532
3.457708
43.25311
4.236817
11
2.125180
3.786838
14.33400
17.86895
10.94853
2.169523
3.211666
42.93782
4.742669
12
2.253894
3.544586
14.34174
18.10975
10.72342
2.276330
3.043029
42.60812
5.353033
13
2.375603
3.332908
14.24427
18.42109
10.51998
2.412283
2.918289
42.17857
5.972617
14
2.493235
3.149433
14.09002
18.78639
10.36775
2.549802
2.829193
41.65050
6.576915
15
2.608774
2.976599
13.93375
19.14822
10.21297
2.683694
2.782035
41.07541
7.187324
16
2.721980
2.820038
13.77899
19.51968
10.05688
2.809336
2.745114
40.51571
7.754246
17
2.832196
2.679669
13.62890
19.87399
9.908516
2.930834
2.709551
40.00218
8.266349
18
2.939349
2.546508
13.48792
20.19775
9.762991
3.046412
2.683171
39.53845
8.736802
19
3.043633
2.422737
13.34821
20.51001
9.617928
3.153068
2.666241
39.10796
9.173850
20
3.144897
2.308987
13.20904
20.81359
9.482067
3.255242
2.655470
38.69793
9.577674
Variance Decomposition of SINTT:
Perio
LN_MCAP
S.E.
DR
LN_GDP
CPI
NSE
LCU_USD
OIL
LINTT
SINTT
1
1.013207
0.054313
4.011428
1.027210
0.045707
5.367935
0.454979
5.400096
83.63833
2
1.214175
0.919387
2.900969
5.753417
0.400063
4.116822
13.89186
4.427524
67.58996
3
1.427489
1.192202
3.555559
9.226947
0.575946
3.003394
14.46328
3.652278
64.33040
4
1.686033
5.569930
4.348320
12.26628
3.360422
2.162695
12.31951
3.858117
56.11473
5
1.878797
6.874018
6.586219
11.69083
4.006623
1.763293
12.52361
5.205762
51.34964
d
261
6
2.078940
8.775574
8.147274
12.11808
3.975778
1.474075
11.53954
8.031639
45.93803
7
2.266647
10.92267
9.077728
12.19317
3.576033
1.245211
10.98668
9.980671
42.01785
8
2.422480
11.17924
9.671710
12.60765
3.411571
1.092669
10.94695
11.24953
39.84068
9
2.571750
11.06603
10.08446
13.66375
3.287489
0.987447
11.23189
11.99842
37.68051
10
2.706415
10.71185
10.45051
14.77073
3.179486
0.892082
11.59504
12.47323
35.92708
11
2.829948
10.35792
10.66586
15.92314
3.196732
0.819662
11.76878
12.74709
34.52081
12
2.946710
10.08913
10.87495
16.93667
3.191495
0.769178
11.99918
13.04318
33.09622
13
3.057922
9.886158
11.03977
17.87915
3.153607
0.735384
12.28834
13.31986
31.69774
14
3.166559
9.763863
11.13032
18.82547
3.111570
0.708377
12.52949
13.53147
30.39943
15
3.272522
9.625251
11.20818
19.72261
3.071643
0.689219
12.78991
13.69788
29.19531
16
3.376507
9.442195
11.28207
20.59910
3.029325
0.673159
13.05459
13.83697
28.08260
17
3.477846
9.248816
11.33877
21.45079
2.984887
0.662454
13.28777
13.95404
27.07247
18
3.575473
9.048068
11.38100
22.24921
2.945272
0.658449
13.49661
14.04897
26.17242
19
3.670102
8.850279
11.40919
23.00149
2.908761
0.658401
13.69909
14.12564
25.34715
20
3.762175
8.666286
11.42423
23.71235
2.872415
0.661896
13.90128
14.18330
24.57825
Cholesky Ordering: DR LN_GDP CPI LN_MCAPNSE
LCU_USD OIL LINTT SINTT
262
ANNEXURE 6: VARIANCE DECOMPOSITION – COMBINED GRAPHS
Variance Decomposition of DR
Variance Decomposition of LN_GDP
Variance Decomposition of CPI
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
2
4
6
8
10
DR
LN_MCAPNSE
LINT T
12
14
16
LN_GDP
LCU_USD
SINT T
18
0
2
20
4
CP I
OIL
6
8
10
DR
LN_MCAPNSE
LINT T
Variance Decomposition of LN_MCAPNSE
12
14
16
LN_GDP
LCU_USD
SINT T
18
2
20
4
CP I
OIL
Variance Decomposition of LCU_USD
70
6
8
10
DR
LN_MCAPNSE
LINT T
12
14
16
LN_GDP
LCU_USD
SINT T
18
20
CP I
OIL
Variance Decomposition of OIL
100
70
60
60
80
50
50
40
60
40
30
40
30
20
20
20
10
10
0
0
2
4
6
8
10
DR
LN_MCAPNSE
LINT T
12
14
16
LN_GDP
LCU_USD
SINT T
18
0
2
20
4
CP I
OIL
6
8
10
DR
LN_MCAPNSE
LINT T
Variance Decomposition of LINTT
12
14
16
LN_GDP
LCU_USD
SINT T
18
20
CP I
OIL
100
60
80
50
40
60
30
40
20
20
10
0
0
2
4
6
8
DR
LN_MCAPNSE
LINT T
10
12
14
LN_GDP
LCU_USD
SINT T
16
18
CP I
OIL
20
2
4
6
8
DR
LN_MCAPNSE
LINT T
263
10
12
14
LN_GDP
LCU_USD
SINT T
16
18
CP I
OIL
4
6
8
DR
LN_MCAPNSE
LINT T
Variance Decomposition of SINTT
70
2
20
10
12
14
LN_GDP
LCU_USD
SINT T
16
18
CP I
OIL
20