Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Macroeconomic Stress Testing for Banking Sector in Indonesia
Noor Irsalina* and Ir. Budhi Arta Surya**
Financial system stability has become main concern since financial crisis
that surge various parts of the world in late 90s. Many central banks and
financial institutions monitor and evaluate their domestic financial
conditions in order to maintain financial stability and banking has significant
role in it.
Macroeconomic condition can affect banking sector stability. Downturn in
economy may deteriorate the quality of bank’s loan. High interest rate and
exchange rate depreciation may cause increase in credit risk, so that the
non-performing loan (NPL) ratio will increase too.
This study aims to measure the linkage between macroeconomic condition
and credit quality in banking sector by using macroeconomic stress testing.
The sample is 101 commercial banks in Indonesia and takes historical
annual data from 2005 until 2012. Macroeconomic stress testing is
conducted by using random effect approach with five variables which are
banks’ Non Performing Loan (NPL), BI rate, GDP Growth, Exchange Rate,
and Inflation Rate.
As the result of panel data regression analysis, the model is insignificant.
This might be because the short period that is used in this research and
other model is actually more appropriate. The BI rate, GDP growth, and
Inflation rate has significant influence towards NPL ratio. The exchange
rate is the only macroeconomic variable that gives insignificant impact
towards NPL ratio.
Keywords: Bank, Macroeconomic, Non Performing Loan, Stress Testing
Field of research: Banking and Finance
1. Introduction
Financial system stability has become main concern since financial crisis that surge
various parts of the world in late 90s. Many central banks and financial institutions monitor
and evaluate their domestic financial conditions in order to maintain financial stability and
banking has significant role in it. The financial sector, which one of it is banking, is central
to almost all macroeconomic debates because behind every real transaction, there is a
financial transaction that mirrors it. Central bank worries about the financial sector
because all the other sectors rely on a functioning financial sector. Every move in financial
sector will give impact to other sector.
Macroeconomic condition can affect banking sector stability. Downturn in economy may
deteriorate the quality of bank’s loan. Since financial crisis in late 90s, central bank needs
to monitor banking sector to know vulnerability early and mitigate it. As mention in
*Noor Irsalina, School of Business and Management, Institut Teknologi Bandung,
Indonesia. Email: noor.irsalina@sbm-itb.ac.id
*Dr. Ir. Budhi Arta Surya, MSc, School of Business and Management, Institut Teknologi Bandung,
Indonesia. Email: budhi.surya@sbm-itb.ac.id
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Financial Stability Review by Bank Indonesia, banking sector in Indonesia should be
aware of potential increase in liquidity risk and credit risk in 2014. High interest rate and
exchange rate depreciation may cause increase in credit risk, so that the non-performing
loan (NPL) ratio will increase too. To measure the linkage between macroeconomic
condition and banking sector, macro stress-testing can be conducted.
In 2005, Indonesian economy grows from last year but slow because there is strong
pressure on macroeconomic stability. The reasons are weakening in the exchange rate
and high inflation rate. But, the Financial System Stability is well maintained. For the next
year, the GDP growth is decrease but inflation rate decrease too. The exchange rate in
2006 maintained an appreciating trend just like in 2005. For the first time after crisis, the
economy growth of Indonesia is above the level of 6% within 2007. This is followed by the
exchange rate stability and the inflation rate is still within target. In 2008, the global
economic crisis began. This condition affects Indonesia and cause high inflation rate. In
the other side, the economic growth still above 6% and the exchange rate is stable. The
global economy still under pressure from the crisis until 2009 and challenge Indonesian
economy. It started to give effect to the GDP growth of Indonesia because the GDP
growth in this year is decrease. But, the inflation rate is in the lowest level in past decade.
In 2010, Indonesian economy is improving. The economic growth increase, exchange rate
is appreciated, but the inflation rate start to increase again. Continued from previous year,
GDP growth reach 6.5% level which is all-time high in the past 10 years and the inflation
rate is decrease. But, the exchange rate still appreciated. Global economic uncertainty
affects Indonesia that still suffering the effects of the global crisis. But, GDP growth
maintain in above 6% and inflation rate increase but still within target. On the other hand,
the exchange rate is depreciated. The volatility of macroeconomic condition over those
years certainly had an impact on credit risk in banking sector.
The main objectives of this research are to know the relationship between macroeconomic
variables and credit risk in banking and measure the impact of macroeconomic variables
to credit risk in bank. This research focus only on BI rate, GDP growth, Exchange Rate,
and Inflation Rate as macroeconomic variable. The bank that will be analyzed is 101
commercial banks in Indonesia with annual data from 2005 until 2012.
In addition of this section 1, this paper also contains other sections as follows. Section 2
explains related theory and studies for the research. The detail of steps to analyse the
data can be found in the section 3. Section 4 discuss about the result of data analysis.
The last, section 5 contains conclusion and recommendation based on the result of the
research.
2. Literature Review
2.1 Financial System Stability
Financial system stability has become main concern since financial crisis that surge
various parts of the world in the late 90s. Many central banks and financial institutions
monitor and evaluate their domestic financial conditions in order to maintain financial
stability.
Schinasi (2006a) defines financial stability as a situation in which the financial system is:
 Allocating resources efficiently between activities and across time.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
 Assessing and managing financial risks.
 Absorbing shocks.
There are four interrelated factors supporting the formation of financial system stability:
a. Stable macro economy
b. Well-managed financial institutions and efficient financial market
c. Sound framework of prudential supervision
d. Safe and reliable payment system.
2.2 Bank
Bank for International Settlement, defines bank as “institution whose business is to
receive deposits and/or substitutes for deposits and grant credits or invest in securities on
their own account.” The functions of banks in Indonesia are basically as financial
intermediary that take deposits from surplus units and channel financing to deficit units.
According to Indonesian banking law, Indonesian banking institutions are classified into
commercial and rural banks. Commercial bank is a bank which based its activities on
conventional and/or Sharia principles in doing so provides services in payment
transactions. Commercial bank consists of state-owned bank, foreign exchange privateowned bank, non foreign exchange private-owned bank, regional development bank, joint
venture bank, and foreign bank. Meanwhile, rural bank is a bank which based its activities
on conventional or Sharia principle in doing so shall not provide any service in payment
transactions.
2.3 Risk
Generally, risk is something that cannot be predicted exactly because of uncertainty. The
major risk that is faced by banking sector is from financial area, which is credit risk, market
risk, and liquidity risk. Credit risk is the risk that a counterparty may not pay amounts owed
when they fall due, market risk is the risk of loss due to changes in market prices, such as
interest rate risk and foreign exchange risk, liquidity risk is the risk that amounts due for
payment cannot be paid due to a lack of available funds (Olsson, 2004).
Credit quality often becomes the dependent variable because the stability of bank mainly
comes from the credit risk. Credit risk can be measured by looking at Non Performing
Loan (NPL) ratio and commonly use as indicator of credit risk to conduct macroeconomic
stress testing for banking. Some of previous study that analyze macroeconomic variable
with NPL ratio are Nkusu (2011), Zeman & Jurca (2008), and Hadad (2005). Non
Performing Loan is the distress loan that failed to be paid by the borrower in 90 days. So,
NPL ratio is total non performing loan divided by total loan.
It is necessary to manage the risk to protect ourselves from the adverse consequences of
a risk event occurring and ensuring that the benefits from taking risks are achieved. To
manage the risk, first organization should identify any risk that may be faced. Then, the
risk should be measured both in order to understand the probability of a risk event
occurring and also to understand the size of impact should it occur. The next step is
organization should decide about what to do, whether managing, accepting, mitigating, or
declining the risk. The last is keep monitoring the risk.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
2.4 Macroeconomic Variable
 GDP Growth
Gross Domestic Product (GDP) is aggregate demand of an economy. A growing
economy is likely to be associated with rising incomes and make financial distress
decrease. So, real GDP growth has negative impact towards NPL.
 BI Rate
BI rate is the policy rate reflecting the monetary policy stance adopted by Bank
Indonesia and announced to the public. BI rate become standard for bank to set
the interest rate. Increase in BI rate can make the borrower more difficult to pay the
credit. Therefore, BI rate is expected to be positively related with NPL.
 Exchange Rate
Exchange rate is the price of the domestic currency expressed in terms of a foreign
currency. The relation between exchange rate and NPL is ambiguous because it
depends on the international trade and the country’s capital account.
 Inflation Rate
Inflation rate is a persistent, ongoing rise across a broad spectrum of prices. The
impact of inflation on NPL can be positive or negative.
2.5 Stress testing
There is no exact definition of stress testing and many experts have their own opinion to
define it. Bank for International Settlement (2000) identifies stress test as the examination
of the potential effects on a firm’s financial condition of a set of specified changes in risk
factors, corresponding to exceptional but plausible events. A stress test is a rough
estimate of how the value of a portfolio changes when you make large changes to some
of its risk factors (Jones & al, 2004). The 1999 BIS document Framework for Supervising
Information about Derivatives and Trading Activities says that scenarios need to cover “a
range of factors that can create extraordinary losses or gains in trading portfolios” and
they should “provide insights into the impact of such event on positions.” One example of
stress testing is macroeconomic stress testing. Its main goals are to identify structural
vulnerabilities in the financial system and to assess its resilience to shock. Some
macroeconomic conditions that commonly used to conduct stress testing are Gross
Domestic Product (GDP), unemployment, inflation, exchange rate, and oil price.
3. The Methodology and Model
3.1 Data Collection
Author collects secondary data for this research from financial ratio of 101 commercial
banks that is available in the Bank Indonesia. The panel data is formed by combining time
series data from 2005 until 2012 and cross sectional data of 104 commercial banks in
Indonesia. For macroeconomic variables, the data is taken from International Monetary
Fund, World Bank, and Bank Indonesia.
Author use panel data because there are some advantages of it over cross section or time
series data. As mention by Baltagi in Gujarati’s book of Basic Econometrics, below is the
advantage of panel data:
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
 There is heterogeneity because panel data relate to individuals, firms, states,
countries, etc.
 Panel data give more informative data, more variability, less collinearity among
variables, more degrees of freedom and more efficiency
 Panel data are suited more to study the dynamics of change because it is studying
the repeated cross section of observation
 Panel data can better detect and measure effects that simply cannot be observed in
pure cross section or pure time series data.
 Panel data enables us to study more complicated behavioural models
 Panel data can minimize the bias.
3.2 Data Analysis
To analyse the data and aim the research objective, author use regression model for
panel data. Panel data is combination between cross sectional data and time series data.
Random effect approach is chosen. NPL ratio becomes the dependent variable while BI
rate, GDP growth, Exchange rate, and Inflation rate become independent variable.
Where
β0 : Intercept
β1 : Slope for BI rate
X1 : Independent variable, BI rate
β2 : Slope for GDP Growth
X2 : Independent variable, GDP Growth
Β3 : Slope for Exchange Rate
X3 : Independent variable, Exchange Rate
Β4 : Slope for Inflation Rate
X4 : Independent variable, Inflation Rate
ε : Error
Because this research use regression model, there are some assumptions that must be
fulfilled, which are:
 Normal distribution
 Homoscedasticity
 No autocorrelation
 No perfect multicollinearity
To know whether the assumptions are fulfilled or not, several tests can be conducted. The
normality test that is conducted in this research use Jarque-Bera test which is a normality
test for large sample where computes the skewness and kurtosis measures of the OLS
residuals. This research use White heteroscedasticity test to know whether there is
heteroscedasticity in the observation or not. It use the value of Obs*R-squared as
measurement. Breusch-Godfrey test is conducted to measure the autocorrelation and use
Obs*R-squared as determination. To know the availability of multicollinearity between
independent variables, this research use correlations coefficient matrix or pair-wise
correlations.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Then, author conduct two test to choose which approach that is more appropriate for this
research, whether it is pooled least square, fixed effect model, or random effect model.
Chow test is a test to choose between pooled least square and fixed effect model, where
Hausman test is a test to choose between fixed effect model and random effect model.
4. The findings
The table below is the descriptive statistic of independent variables
Table a. Descriptive Statistic
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Sum
Sum Sq. Dev.
Observations
BI_RATE
0.071409
0.068646
0.118330
0.000860
0.033239
-0.709156
3.187252
57.69864
0.891624
808
EXCHANGE_RATE
9430.589
9272.975
10389.94
8770.430
480.6249
0.659452
2.555693
7619916.
1.86E+08
808
GDP_GROWTH
0.058902
0.061188
0.064904
0.046289
0.005691
-1.145468
3.288671
47.59316
0.026141
808
INFLATION_RATE
0.062919
0.058921
0.133317
-0.048950
0.051752
-0.837901
3.263757
50.83838
2.161345
808
To test normal distribution, author conduct normality test using Jarque-Bera test. The
value of Jarque-Bera is 105.91 which is less than the value of Chi-Square, so it can be
concluded that the residual is normally distributed. After that, heteroscedasticity test is
conducted using White heteroscedasticity test. From the result if this test, the value of
Obs*R-squared is higher than the value of significant level, it means that there is no
heteroscedasticity in the model. Meanwhile, Breusch-Godfrey test is conducted to
measure the autocorrelation in this research and give result that there is no
autocorrelation. The last test is to know the availability of multicollinearity between
independent variables that use correlation coefficient matrix. From the matrix, there is no
value that more than 0.8, so it can be concluded that there is no multicollinearity between
independent variables.
After all assumptions are fulfilled, author conducts two tests to choose appropriate
approach that should be used in panel data regression. From the result of Chow test and
Hausman test, this research should use random effect approach. Then, regression
analysis using random effect can be performed. The table below (Table b) is the summary
result of regression analysis using random effect.
Table b. Regression result
VARIABLE
C
BI rate
GDP growth
Exchange rate
Inflation rate
COEFFICIENT
0.032924
-0.026663
-0.464739
5.97E-07
0.050405
T-STATISTIC
2.0015
-1.8538
-4.0409
0.5272
5.5633
PROBABILITY
0.0457
0.0642
0.0001
0.5982
0.0000
It can be written that the result of regression analysis using random effect model is below
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Based on the result of regression using random effect model, the model is not significant.
This might be because author only use short period for the model and another model is
more appropriate for this research.
To know whether the independent variables have significant impact towards dependent
variable or not, it can be seen from the value of F-statistic. The value of F-statistic is
15.23624 which are higher than the value of F table, so it can be concluded that the
macroeconomic variables simultaneously have significant impact toward NPL ratio.
T test is applied to determine whether regression coefficient individually has significant
impact towards dependent variable. From the result of regression analysis (Table a), the
BI rate and GDP growth has negative significant impact towards NPL ratio. The Inflation
rate has positive significant impact towards NPL ratio. Meanwhile, Exchange rate is the
only independent variable that has positive insignificant impact towards NPL.
The sensitivity analysis of using random effect regression model for NPL ratio can be
described as below:
 Every increase of 1% from BI rate, will make NPL decrease 2.67%
 If GDP growth increase by 1%, NPL will decrease 46.47%
 Every increase of 1% from Exchange rate, NPL will increase by 0.000059%
 If Inflation rate increase by 1%, the NPL increase 5.04%
5. Summary and Conclusions
The objectives of this research are to know the relationship between macroeconomic
variables and credit risk and to measure the impact of macroeconomic variables to credit
risk in banking sector. Thus, the independent variables are from macroeconomic
conditions which are Bi rate, GDP growth, Exchange rate, and Inflation rate. Meanwhile,
the dependent variable is NPL rate which is one of measurement of credit risk in bank.
As the result of panel data regression analysis using random effect model, the model is
insignificant, might be because the short period that is used in this research and other
model is actually more appropriate. The BI rate, GDP growth, and Inflation rate has
significant influence towards NPL rate. The exchange rate is the only macroeconomic
variable that gives insignificant impact towards NPL ratio. The GDP growth has the
biggest impact in banks’ stability, in term of credit risk, in Indonesia.
The result of this research can be used for banks in Indonesia to mitigate the credit risk
that is caused by macroeconomic condition. Indonesian bank also can monitor the
movement of GDP growth because it gives biggest impact in banks’ stability. By
monitoring and mitigating, bank can minimize the consequence of the risk.
For further research, it is better to add more macroeconomic variables and take longer
period. Other researcher also can use different model that is more suitable since the
model in this research give insignificant impact.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
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