DISSERTATION ON
Impact of Selected Macroeconomic Variables on the Performance of
Indian Stock Markets: Pre and During Covid study
A Time Series analysis
Submitted
For the partial fulfilment of the Degree of
MA ECONOMICS (2020-22)
(Specialization in International Trade & Finance)
By
Prithvi Venkataraman
Under the Guidance of
Dr. Niti Nandini Chatnani
MAY 2023
INDIAN INSTITUTE OF FOREIGN TRADE
IIFT BHAWAN, B-21, Qutab Institutional Area, New Delhi- 110016
1
DECLARATION
This is to certify that I, a student of MA Economics (2021-2023), Indian Institute of
Foreign Trade, New Delhi, have submitted this research project “Impact of
Macroeconomic variables on the performance of Indian stock market; Pre and During
Covid study; A Time Series analysis” to IIFT in partial fulfilment of the requirements
for the MA Economics degree. This is an original work. It is neither copied (partially
or fully) from any other scholastic work nor it is submitted to any other institution for
any degree or diploma. I remain fully responsible for any error and plagiarism.
Prithvi Venkataraman
MA Economics 2021-2023
New Delhi
Guide Certification
This is to inform that Prithvi Venkataraman, student of MA Economics 20212023, has completed research project on the topic “Impact of Macroeconomic
variables on the performance of Indian stock market; Pre and During Covid
study; A Time Series analysis” under my guidance.
Niti Nandini Chatnani
Date:
2
ACKNOWLEDGEMENT
I would want to express my heartfelt gratitude to the individuals mentioned below, without
whom I would not have been able to write my dissertation or complete my master's degree.
Writing my thesis has been difficult, but it has also been tremendously gratifying on a
personal level as well as educationally. I am excited to participate in this study and learn from
it.
First and foremost, I would like to thank my supervisor Dr. Niti Nandini Chatnani, whose
insight and knowledge of the subject matter steered me through this research. Her guidance and
support throughout this dissertation were remarkable.
Also, I would like to thank my family and friends who were patient with me during all these
months of dedicated research and always extended their valuable support.
3
Contents
Chapter 1 : INTRODUCTION……………………………
5-11
Chapter 2 : LITERATURE REVIEW…………………….
12-14
Chapter 3 : DATA AND VARIABLES……………………
15-26
3.1 The variables………………………………………….
27
3.1.1 Dependent variable…………………………………
27
3.1.2 Independent variable……………………………….
27
3.2 Methodology…………………………………………
28
3.2.1 Augmented Dickey Fuller (ADF) unit root test……
28
3.2.2 OLS and significance………………………………
29
3.2.3 Descriptive statistics……………………………….
30
3.2.4 Correlation matrix………………………………….
33
3.2.5 Auto Regressive Distributed Lag model (ARDL)
cointegration technique…………………………………..
34
3.2.6 Gregory and Hansen Cointegration test……………
39
Chapter 4 : CONCLUSION……………………………...
42
References………………………………………………..
45
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CHAPTER 1
INTRODUCTION
1.1 Background
Macroeconomics refers to the field of economics that examines how an entire economy
functions and performs. It specifically concentrates on overall changes within the
economy. Moreover, macroeconomic variables serve as key indicators that reflect the
current trends in the economy. These variables include unemployment, growth rate, gross
domestic product (GDP), and inflation, among others.
To effectively manage the economy at a macro level, the government, like any other
experts, needs to thoroughly examine, analyze, and comprehend the significant factors that
influence the present state of the macro-economy. This requires understanding the drivers
of economic growth, predicting future trends, and determining the most appropriate
combination of policies to maintain stability within the economy.
The focus of the study however is to see how these macroeconomic variables impact the
Indian stock market. So, we look at this in detail:
 Since 1991, when the government began implementing the Liberalisation,
Privatisation, and Globalisation Model in India, the stock market has experienced
several ups and downs. This approach has united all nations, and as a result, a
massive, globally interconnected market has been produced. Due to the integration
of the world economies in the current period, numerous domestic and foreign
factors have a direct or indirect impact on the performance of the stock market.
 • As a result, the stock market is becoming more and more significant since it
facilitates the flow of capital between developing and emerging economies, which
in turn stimulates the growth of an economy and industry. This also applies to the
Indian stock market. The performance of the economy is impacted by even the
smallest stock market fluctuation. Investors can provide or take the assets (funds)
for capital appreciation in the capital market, regardless of whether they are Indian
or foreigners. Before investing his money in the stock market, an investor
considers a number of criteria. The previous performance of a company, return on
index or by company, return on assets or equity, free cash flow, internal
management, and different macroeconomic factors like GDP, inflation, interest
rate, etc. are just a few examples of the many variables that may be present.
Macroeconomic variables are one of the many elements that influence stock
market investing decisions, and certain macroeconomic factors have a big impact
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on stock return while others have a minor one.
 Primary market and secondary market are two ways to categorise the market. The
primary market is where different businesses and the government sell securities for
the first time; the secondary market is where these assets are later sold.
 Stock indexes are what we are using to represent the stock market. An index's
primary function is to track price changes. In a similar vein, a stock index will
reflect changes in stock price.
 A rising index shows that investors anticipate higher profits from organisations, as
equities should represent what businesses expect to earn in the future. Additionally,
it serves as a gauge for the state of the Indian economy. A rising index shows that
investors anticipate higher profits from organisations, as stocks should represent
what businesses intend to earn in the future. Additionally, it serves as a gauge for the
state of the Indian economy.
 Following globalisation, the trajectory of the Indian stock market accelerated too
quickly, making it a global investment magnet.
A part of financial markets are stock markets. By allocating resources and generating
liquidity for companies and entrepreneurs, they play a crucial part in facilitating the
smooth operation of capitalist economies. Additionally, without them, it would be
difficult or impossible to distribute capital effectively and there would be a significant
negative impact on or reduction in economic activities like trade, investments, and
growth possibilities.
We discuss the macroeconomic variables and their relationship with the stock market
elaborately in the data section.
How covid plays a pivotal role in our study
What changed after Covid?
For all nations, whether it be the world's superpower, the United States of America, or the
contender for the title, China, COVID-19 is a game-changing event. The World Health
Organisation (WHO) proclaimed COVID-19 a pandemic on March 11, 2020. Since the
start of the COVID-19 epidemic, all economies have been shut down completely or in part,
and residents have been put on lockdown for months. As a result, national income,
employment rates, and overall industry production have declined in both emerging and
developed countries.
When the pandemic hit the countries, the stock markets immediately saw a drop in stock
prices and a rise in volatility. Partial lockdowns had a significant negative influence on the
financial markets, which in turn had a negative impact on the developing countries'
economic operations. It was also seen in many nations that some industries were operating
incongruously better than other severely impacted industries, such pharmaceuticals, and
postal services.
Due to public anxiety over diminishing economic activity, less disposable income, and
6
unfavourable investor sentiment, the stock market's financial performance is anticipated to
worsen under crisis-like events, such as pandemics. Reduced liquidity and lower returns
are two ways that the general market benchmark reflects these consequences. On the other
hand, due to variances in industries and responses to the macroeconomic stimulus, sectoral
performance may deviate from the benchmark index. Understanding how COVID-19
affected international financial markets is crucial because it turned out to be a crucial event
for the entire world. The analysis can assist us comprehend how the pandemic-related
attitudes and anxieties of investors influenced the performance of the Indian stock market.
For India:
The market capitalization of listed firms fell by 23% on each of the National Stock
Exchange (NSE) and Bombay Stock Exchange (BSE) in March 2020. Stock prices
generally continued to fall on the financial market in March 2020.
The BSE Sensex and NSE Nifty both plummeted by 38% after the COVID-19 outbreak as
investors lost confidence. The result is a loss of the entire stock market of 27.31% since
the year's commencement.
A global stock market crisis happened at the start of 2020 against the backdrop of COVID19. The resultant global liquidity crisis was brought on by the progressive spread of the
national debt, the gold, and crude oil markets, all of which experienced price crashes to
varying degrees, as well as by the liquidity exhaustion brought on by the stock market fall.
Existing research demonstrates that when there is a negative shock, there appears to be risk
transmission between markets, and the risk spill over effect increases significantly. As a
result, when one financial market is negatively affected, the negative effect spreads quickly
to other institutions and markets, leading to systemic financial risks.
During the first lockup in 2020, the Indian stock market moved downward. News about
diseases consequently affected the stock market. Due to the Indian government's easing of
some lockup laws, it then experienced a progressive rising trend in 2021. These findings
highlighted how COVID-19 and macroeconomic variables both affected the performance
of the Indian stock market. The current study considers the simultaneous effects of all
elements, including exchange rate volatility, crude oil price, etc., since there are few
studies in the Indian context that measure the influence of macroeconomic variables and
pandemic crisis on the stock market's performance.
In data, we dive deeper as to why only certain macroeconomic variables were chosen and
also how they affect the stock market (backed by theories), building on the structure of the
analysis.
1.2 Research Aim and Objectives
Research Aim:
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The aim of the research is to estimate the Impact of Macroeconomic variables on the
performance of Indian stock market; Pre and During Covid (the transition). To fulfil the
research objectives, we will employ a time series analysis to explain and quantify the
effect of Macroeconomic variables on the performance of Indian stock market during
the following 2 periods:
 Pre covid period
 During Covid period
For this purpose, 4 macroeconomic indicators have been carefully picked to scrutinize
the Indian stock market (between 2015-2022). The structural break in the model due to
Covid-19 is also solidified with econometric analysis.
1.3 Significance of the Study
The possibilities for research in the numerous subfields of finance are virtually endless.
The Covid 19 Pandemic is one of the recent key events that has had a notable impact on
the stock markets the banking industry. The worldwide economy was impacted by the
lockdowns and other limitations that were put in place. On significant markets all across
the world, it had a profoundly detrimental effect. The interconnectedness of the global
markets and financial integration made it so that within a few months, the aftereffects
had a significant impact on all the financial markets worldwide.
The necessity to study financial asset volatility and the effects of information spillover
from one economy to another in the context of this epidemic was therefore seen by
policymakers and portfolio managers as urgent. The study would be extensive and cover
a range of genuine problems that must be solved in the context of the finance which
would help in deriving implications in order to see where we must improve and further
how the economies can recover from the hit of the pandemic.
Further to precisely highlight the importance of carrying out this study we discuss the
following areas:
The following points must speak about how the role of selected macroeconomic
variables on the stock markets has changed due to COVID.
1. Economic analysis: The stock market is a key indicator of the overall health and
performance of the economy. By studying the impact of COVID-19 on the Indian
stock market, analysts and policymakers can gain insights into the broader
economic consequences of the pandemic. It helps them understand the extent of
the disruption caused to various sectors, assess the financial stability of
companies, and identify potential vulnerabilities in the economy. When policy
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makers can figure out how their economy reacts to a pandemic or a contingency,
we can be well prepared to handle such situations in the future and feasible
emergency mechanisms can be put in place (so that the situation does not
escalate).
2. Investor decision-making: Investors, both individual and institutional, rely on
the stock market to make investment decisions. Understanding how COVID-19
has influenced the stock market can help investors assess the risks and
opportunities associated with different sectors and companies. It provides them
with valuable information to make informed investment choices and manage their
portfolios effectively. Investors make up a very integral part of the financial
market, their decisions (risk averseness) serve as the benchmark as to how the
sentiment is for the whole economy.
3. Policy formulation: Governments and regulatory bodies monitor the stock
market closely to evaluate the effectiveness of their policies and interventions.
Even when a pandemic recedes, it leaves behind a lot of its impact on the
economy as well as its citizens, so to say that the uncertainty remains. This can
force citizens to exercise more caution in their financial decisions. The general
public starts saving more to support them in unforeseen circumstances that might
appear in the future, since pandemics take time to completely end. By studying
the impact of COVID-19 on the Indian stock market, policymakers can assess the
success of stimulus packages, regulatory measures, and other initiatives aimed at
stabilizing the market and supporting economic recovery. This knowledge helps
in refining existing policies or developing new ones to address emerging
challenges.
4. Sector-specific analysis: COVID-19 has had varying impacts on different sectors
of the economy. Some sectors, such as healthcare, pharmaceuticals, and
technology, have experienced growth and resilience during the pandemic, while
others, such as hospitality, aviation, and retail, have faced significant challenges.
Studying the impact of COVID-19 on the Indian stock market allows for a sectorspecific analysis, providing insights into the winners and losers in the market and
guiding strategic decisions for businesses within those sectors.
5. Risk management: Global financial markets are now extremely unstable and
volatile due to COVID-19. Investors and financial institutions might better
understand the risks involved with specific investments by researching how the
epidemic affected the Indian stock market. It aids in the creation of risk
management plans, portfolio diversification, and the application of suitable
hedging measures to reduce possible losses.
6. Capital outflows: The pandemic triggered significant capital outflows from
emerging markets, including India. Foreign institutional investors (FIIs) sold off
their Indian equity holdings to mitigate risks and manage liquidity concerns. This
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outflow of foreign capital put pressure on the Indian financial markets and led to
a decline in stock prices. Huge capital withdrawals have an impact on the
domestic currency's exchange rate, which causes the domestic currency to
depreciate. When money leaves the country, more people exchange their local
currency for foreign currency. The value of the local currency decreases as a
result. This can really impact the foreign reserves of a country. We also focus
significantly on this aspect of exchange rate and its relationship with the stock
market.
Overall, studying the effect of COVID-19 on the stock market in India provides valuable
information for economic analysis, investor decision-making, policy formulation,
sector-specific analysis, and risk management. It enables stakeholders to make informed
choices and take necessary actions to navigate the challenges posed by the pandemic
and support the recovery of the economy and financial markets.
1.4 Research Gap
When relevant research papers were studied (some of the papers you studies should be
mentioned here. Research Gap identification always follows a Literature Review), then
it was found that sufficient study has not been conducted on the topic and that the
existing studies revolve only around covid 19 along with its effect on Indian stock
market and not on examination of how the Indian stock market is affected by
macroeconomic variables. The above two topics are very different from each other, the
first one being a little simpler since it is not multidimensional whereas the latter being
more complex since it involves many dynamic variables like money supply, oil price
etc. which effect the stock prices significantly and must be accounted for in a proper
manner. Further research will also yield the hidden relationships (not yet discovered)
between the variables we have taken (money supply, interest rate, inflation etc.
mentioned before) and the stock market; therefore, the project would be as practical as
possible hence being more relevant in the current economic scenario.
The study of macroeconomic variables on the Indian stock market pre and during the
COVID-19 pandemic has been a subject of elaborate studies. While noticeable progress
has been made in understanding how the macroeconomic indicators and the stock
market performance are related , several research gaps still exist. Here are some key
areas where further investigation is needed:
1. Causality: One research gap involves validating the direction of causality. It is
essential to determine how each key indicator effects the stock market indices.
Additionally, how these relationships were affected due to the pandemic is the
key focus of our study, which will address the important notions on the above
concern.
2. Investor sentiment: How Covid-19 caused the investors to become more risk
averse which consequently influenced their decisions which showed up as the
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volatility in stock markets explaining why the change in the value of indices need
to be taken into consideration.
3. Policy implications: We wind up the study by suggesting policy implications on
how macroeconomic indicators do provide a way to explain how the stock market
dynamics work. And this process experienced a noticeable shift pre and during
covid. Some policy areas which need to be focused upon keeping the situations
in mind will also be sufficiently addressed.
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CHAPTER 2
LITERATURE REVIEW
 The Indian context:
The patterns of the days of the week have been thoroughly studied in many markets.
The distribution of stock returns changes depending on the day of the week, according
to studies (Aggarwal & Rivoli, 1989; Cross, 1973; French, 1980; Keim & Stambaugh,
1984; Rogalski, 1984).
According to the "weekend effect" (Dubois & Louvet, 1996; Gibbons & Hess, 1981),
stock returns are abnormally higher on some days of the week than on other days.
Choudhry (2000) looked studied the impact of the day of the week on the returns and
conditional variance (volatility) of seven rising Asian stock markets. Daily returns from
India, Indonesia, Malaysia, the Philippines, South Korea, Taiwan, and Thailand were
employed in this study along with the GARCH model. For this investigation, the data
from January 1990 to June 1995 were used. It discovered that the day of the week had an
impact on :
both stock return and volatility.
The effect may be caused by a potential spill over from Japanese stock, despite the fact
that both the return and volatility are not the same in all seven instances. Indian Stock
Market During the COVID-19 Pandemic: Vulnerable or Resilient? Rishika Shankar and
Priti Dubey (2021). This study examines the impact of the COVID-19 epidemic on the
daily average returns and trade activity on the Indian stock market through sectoral
analysis. Because of the scant or non-existent economic activity, businesses, and the
government both faced financial difficulties (as was originally expected). In Yashraj
Varma, Renuka Venkataramani's (2021) article, "Short-Term Impact of COVID-19 on
the Indian Stock Market," the author sought to ascertain how the pandemic may influence
the market's main index (NIFTY50) and the various sectors over the short time. All
sectors of the economy were temporarily impacted, but the financial sector was hardest
damaged. The study found that several businesses, including medicines (the Covid
period's most sought-after industry), consumer goods, and IT, had favourable or
negligible effects.
 The world context:
12
To gain a bigger picture: Shanken and Weinstein (2006) came to the conclusion that the
only major element for stock markets is the Index of Industrial Production, which
makes sense given that it allows us to understand the general attitude of investors
towards the industries being taken into account. Using cointegration analysis, Gan, Lee,
Yong, and Zang (2006) examined the correlation between seven macroeconomic factors
and the New Zealand Stock Index from 1990 to 2003. The study's findings revealed that
two of the eight factors used to construct the New Zealand Stock Index are interest rate
and money supply. Numerous academics have also attempted to quantitatively account
for the stock's volatility. The ARCH was presented by Engle in 1982, the GARCH by
Bollerslev in 1986, and the EGARCH by Nelson in 1991.
R. Mookerjee and Q. Yu (1997) examined the correlation between macroeconomic
variables and Singapore stock returns using monthly data for four macroeconomic
indicators, including the broad money supply, foreign reserves, narrow money supply,
and exchange rate, during the time period from October 1984 to April 1993. Their
research revealed that while exchange rates did not exhibit a long-term link with stock
market returns, foreign reserves, broad and narrow money supplies did.
Humpe and MacMillan (2007) did an analysis for the US and Japan spanning the years
1965–2005. Using cointegration analysis, it was discovered that stock prices in the US
have a positive correlation with industrial production and a negative correlation with the
consumer price index (CPI). These findings are supported by the relationships between
stock prices and industrial indexes as well as inflation (measured by the Consumer Price
Index, CPI, and stock prices) that we have already defined. In Japan, the money supply
has a negative impact on stock prices while the industrial production index has a positive
impact. However, it is noteworthy to note that the consumer price index and long-term
interest rates have a negative impact on industrial output. Using the rolling-sample
cointegration technique and VAR parameters, Laopodis (2011) conducted an analysis for
pre and post Euro eras in France, Germany, Italy, the UK, and the US throughout the
period of 1990–2009.
As a result, it was discovered that the stock indices of various nations responded
differently to changes in economic fundamentals, particularly in the post-Euro era, as a
result of the clear differences in the economic conditions and the various forms of
governance that can give rise to various deviations in fundamental behaviour.
Exchange rate and all stock market indexes were found to be causally related in both
directions by Aydemir and Demirhan (2009). Anh and Gan (2020) used panel data
from 723 listed company returns in Vietnam to assess the effects of the COVID19
outbreak and subsequent lockdowns. Because a pandemic of this magnitude has never
affected the financial markets, the analysis found large changes in returns before and
after the COVID-19 outbreak. This was expected, and the lack of preparation resulted
in much worse consequences than we would have seen. According to Cox et al.
(2020), the US stock market volatility appears to have been mostly caused by quickly
13
shifting investor sentiment or attitudes towards risk that were unrelated to economic
fundamentals and policy actions. This may be explained by the fact that most stock
market participants are investors and stock brokers, and that their behaviour or actions
may have a significant role in predicting stock market swings before or even during a
pandemic.
Taking a slight diversion, we will consider a study that focused on the global financial
crisis since, although Covid 19 was a pandemic, it nevertheless had long-lasting
consequences, much like a crisis would. The relationship between the stock returns of
Brazil, Russia, India, China, and South Africa and the price of gold and oil was studied
by Naeem et al. in 2020. The daily statistics from January 2002 to December 2018 were
taken into consideration for this inquiry. For the time before, during, and after the global
financial crisis (GFC), they also used the quantile-on-quantile regression (QQR) and
quantile coherency (QC) techniques. They demonstrated that, prior to the Great
Recession, there was essentially no dependence on stock and oil returns for the middle
quantile. However, it was shown that there was a significant correlation between oil
prices and stock returns across the board during and after the Great Financial Crisis.
In addition, the pre-GFC period showed a less positive correlation between gold and stock
returns, whereas the subsequent period saw a strengthening of that correlation. The
ramifications of Covid 19 can be examined in a similar manner. A study published in
Resources Policy Volume 79, 2022 by Cui Xiaozhong, Kuo Yen-Ku, Apichit
Maneengam, Phan The Cong, Nguyen Ngoc Quynh, Mohammed Moosa Ageli, and
Worakamol Wisetsri aims to estimate the dynamic relationship between oil prices, gold
prices, oil price volatility, and gold price volatility on the Chinese stock market.
The study used the Autoregressive Distributed Lag (ARDL) bound test approach to use
daily data from 2009 to 2021 for the aim of empirical estimate. For asymmetric estimating
that is more thorough, Nonlinear ARDL and asymmetric Causality analysis have also
been used. According to the results of our analysis, oil and gold prices have a long-term
detrimental impact on the Chinese stock market. According to the implied volatility index
for these commodities, the study discovers that the price volatility of gold has a longterm beneficial impact on the country's stock market while the price volatility of oil has
a negative impact. However, only the prices of gold and oil have a long-term impact on
the Chinese stock market. Based on our research, we advise investors to respond to
market concerns rationally and to think of gold as a safe haven in which to protect
themselves. In order to deal with the rapid uncertainty flow of information from the oil
to the stock market, policymakers should develop suitable measures and methods.
14
CHAPTER 3
DATA AND VARIABLES
The stock market serves as a means for companies to raise capital by selling shares to
investors in the form of dividends. This further acts as an impetus to fuel the growth and
sustenance of a company as well as the reason for why entrepreneurs should employ
their creativity and look for profitable business plans and enter the industry. It also acts
as an appealing way for people to invest their funds in and earn money by carefully
studying the patterns that prevail in the stock market. The stock market indexes are
highly fluctuating and are indicative of various crucial sectors that make up the
economy.
Also, once funds are invested in a company at a particular time some return on these
funds are returned as dividends that the companies declare according to their profit.
These funds then encourage the individual/companies to save. Excess funds are then
invested and this cycle goes on. However, this is what sustains the economy. Without
investment the integral economic activities will not sustain and its basic mechanism
would collapse.
The equation we are studying is as follows:
Niftyt = α + 1ExRatet + 2GoPrit + 3GDPGrt + 4VIXt
where t= time, for time series analysis.
The variables are:
ExRate: represents the exchange rate
GoPri: gold price in India
GDPGr: Gross Domestic Product (GDP) growth rate of India
VIX: Volatility Index of India
In the pool of so many macroeconomic indicators we have used Exchange rate, gold
price, GDP growth rate and Volatility Index to represent the movements or variation in
the Indian stock market represented broadly by the NIFTY index. The subscript is t to
represent the time series nature of our analysis. Here, we do the study of the dependent
variable as time varies between the time period selected.
15
 1: Represents the change in the nifty index corresponding to 1 rupee change in
the exchange rate holding all the other variables constant.
 2: Represents the change in the nifty index corresponding to 1 rupee change in
gold price per 10 grams holding all the other variables constant.
 3: Represents the change in the nifty index corresponding to 1% change in the
GDP growth rate holding all other variables constant.
 4: Represents the change in the nifty index corresponding to 1 rupee change in
the price of S&P 500 holding all other variables constant.
In theory there are many other variables that effect the stock price like Oil price, interest
rate money supply etc. however we limit to the variables mentioned above to make the
study as precise and comprehensive as possible. The independent variables are taken to
be as relevant as possible and also have theory and logic supporting their impact on the
dependent variable under consideration.
3.1
The variables:
3.1.1 Dependent variable:
Nifty (NIFTY 50) is the variable under consideration that represents the Indian stock
market comprehensively. Nifty stands for National Stock Exchange Fifty. It is the equity
benchmark index of the National Stock Exchange (NSE) of India.
The Nifty 50 is a collection of stocks selected from approximately 1,600 actively traded
companies on the National Stock Exchange (NSE) spanning 24 sectors which broadly
represent the Indian economy. It consists of the top 50 stocks based on certain criteria
such as market capitalization, liquidity etc.
The independent variables together explain the change in the nifty variable and thus we
arrive at results accordingly. SENSEX comprises of only the top 30 companies actively
trade in the Bombay stock exchange (BSE) and it covers a narrow range limiting to only
13 sectors. Hence, Nifty 50 was considered over SENSEX for the dependent variable.
The data was obtained monthly from 2015-2022. Then it was converted to quarterly data
by averages and then reported.
Unit: It indicates the closing price of the 50 stocks at the given period of time.
(rupees).
Source: Yahoo Finance
3.1.2. Independent variables:
16
1) Exchange rate
First independent variable is the exchange rate which is the most variable in our study
that explains the stock market movements.
And hence the unit simply put is in rupees as it was for the nifty index for uniformity.
The data was not manipulated as the format given was already in quarters. (It was
given as the average of daily rates) The exchange rate can either depreciate or
appreciate. Depreciation would signify that the value of the currency falls, so more of
it is now required to buy the foreign currency. This can boost the stock prices, as
depreciation means cheaper export price, more profits for firms increasing the stock
price. On the other hand, it increases the cost of imports, which can reduce the profits
of firms relying on them reduce stock dividends. This increases the price of imported
goods which can cause inflation so reduce real return on the stock market funds.
Therefore, according to theory, they have an inverse relationship. The reverse holds
true for when the currency appreciates, then the stock market thrives as imports are
cheaper.
Unit: Exchange Rate is in the form of National currency to US Dollar exchange Rate
which means rupees per unit US $.
Source: Federal Reserve Economic Data
2) Gold Price
The predominant or traditional ways in which trends are usually invested is
Gold derivatives
Stock market
Gold is usually used as a substitute for the stock market and it is usually also prone to
less volatility as the stock market is representative of many global factors as well as
domestic determinants. An increase in gold price per unit quantity might cause the
investors to shift from the stock market as gold becomes an attractive means to
investment. This can cause the stock index to fall or stock market to face recession wave.
According to studies the stock market and gold prices have a negative relationship. The
above is due to the investor’s idea of the market in general. Investors can use gold as a
hedge to reduce the risks faced in the stock market investment process. The data was in
monthly format i.e., close price for the last day of the month. So, averages were taken
for three months to convert into quarterly format. Like other financial markets, gold has
served as a form of protection against the negative impacts of a crisis in the Indian
17
markets, particularly when traditional assets like stocks become volatile or uncertain.
Unit: it is in rupees per 10 g of 24 Karat (purest form of gold used for investment and
trading processes solely).
Source: Gold Price India
3) GDP growth rate
GDP also known as the gross domestic product quantifies the financial worth of end
products and services, encompassing those acquired by the ultimate consumers,
generated within a country's boundaries during a specific timeframe, such as a quarter
or a year (usually preferred). It covers the entire output produced within a country's
borders.
Therefore, the GDP growth rate of India would mean the yearly mean rate of fluctuation
in the GDP at market prices (rupees), adjusted for inflation or price changes, within a
particular economy, over a defined period. The stock market is highly dependent on the
sentiment of investors across the economy towards saving and investing. If there is
significant GDP growth rate therefore the economy is in boom, people have sufficient
money in hand which also means a positive sentiment of investors towards the stock
market. Therefore, GDP growth rate is positively related to the stock market indices. If
GDP growth rate rises the stock market will also face a boom. The data was available
quarterly for India at inflation adjusted rate (actual rate) that was documented.
Unit: since it is a rate therefore it is in percentage terms (%) (percentage change in real
GDP per capita of between two consecutive years) whereas GDP of India is documented
in trillions.
Source: Ministry of Statistics and Programme Implementation
4) Volatility Index of India
Volatility Index is a measure of the market's expectation of volatility over a certain time
period. It is explained as the 'rate and magnitude of changes in prices' and represents
risk. India VIX is a volatility index based on the NIFTY Index Option prices. From the
best bid-ask prices of NIFTY Options contracts, a volatility figure (%) is calculated
which indicates the expected market volatility over the next 30 calendar days.
India VIX is a measure of volatility derived from the prices of NIFTY Index Options.
By analysing the most favourable bid-ask prices of NIFTY Options contracts, a
percentage value representing volatility is computed, reflecting the anticipated market
volatility in the upcoming month. As is already visible, the greater the volatility or VIX
the more critical the investors are with their funds due to the uncertainty in the market
and hence the stock market faces a low period and stock indices can go down. So, they
have a negative relationship in theory. Even in the context of India, the VIX data will
confirm the volatility and therefore the trend expected to prevail in the stock market.
The data obtained was monthly converted to quarterly format by averaging and then
18
considered.
Unit: It is measured in VIX points. One VIX point represents one percent per annum in
the implied volatility of the S&P500 index.
Source: Investing.com
The variables are officially documented with the following criterions accounted for Their measurement unit and Type:
Table 1 variables and their unit of measurement
Variable of Interest
NIFTY
Type of Variable
Exchange rate
Independent Variable
National currency to US Dollar
exchange Rate (rupees per unit
US $)
Gold Price
Independent Variable
Rupees per 10 g of 24 Karat
GDP growth rate
Independent Variable
Percentage change in the real
GDP per capita. (%)
Volatility Index of India
Independent Variable
In VIX points
Dependent Variable
Measurement
Closing price of the 50 stocks
at the given period. (rupees).
We also understand the nature or trend of the variables first by plotting the scatterplot of
the variables with time ranging from 2015-2022 :
19
Dep Variable nifty
20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
2015
2016
2017
2018
2019
2020
2021
2020
2021
2022
Exchange Rate
84
80
76
72
68
64
60
2015
2016
2017
2018
20
2019
2022
GDP growth rate
30
20
10
0
-10
-20
-30
2015
2016
2017
2018
2019
2020
2021
2022
2021
2022
Gold Price
55,000
50,000
45,000
40,000
35,000
30,000
25,000
2015
2016
2017
2018
21
2019
2020
volitality index (VIX)
40
35
30
25
20
15
10
2015
2016
2017
2018
2019
2020
2021
2022
Research Questions
Research Question 1
The regression model is of the following form:
Niftyt = α + 1ExRatet + 2GoPrit + 3GDPGrt + 4VIXt
where t= time, for time series analysis.
The variables are:
ExRate: represents the exchange rate
GoPri: gold price in India
GDPGr: Gross Domestic Product (GDP) growth rate of India
VIX: Volatility Index of India
Before we find out the OLS or significance of the variables, we ensure we find out the
stationarity (meaning addressed later) of the variables. All the variables should be
stationary or i(0) in order to test the significance or to apply the OLS method.
The Ordinary Least Squares (OLS) approach refers to a linear regression method
22
employed for estimating the unknown parameters (’s) within a model. This technique
further involves minimizing the total sum of squared differences between the observed
values of the dependent variable and the corresponding predicted values generated by
the model (i.e., error that is difference between population parameter(actual) and sample
parameter(predicted) through the regression.
So through Augmented Dickey Fuller Unit Root Test we check for stationarity and then
employ OLS.
Further we use graphs to see the
1) trends in the variables
2) relationship of the Dependent variables with the independent variables
So, first research question addresses the significance of all the variables through OLS
(ordinary least square method) and other descriptives statistics like skewness, kurtosis,
mean, median, mode etc.
The data has been taken from the time period between the year 2015 to 2022 in quarterly
format:
2015 Q1 (start of the year) to 2022Q4 (end of the year), i.e., 32 observations (minimum
30 data points are required for time series analysis). The t in the regression equation
represents the time series aspect of the data that varies to study the relationship between
the variables in the regression.
The rationale here is to study the effect of covid on the relationship between the
macroeconomic indicators and the stock market, studied through the coefficients.
Research Question 2
The regression model is of the following form:
Niftyt = α + 1ExRatet + 2GoPrit + 3GDPGrt + 4VIXt
Where t is the time period between the year 2015 to 2022 in quarterly format so
2015 Q1 (start of the year) to 2022Q4 (end of the year), i.e., 32 observations (minimum
30 data points are required for time series analysis)
where t= time, for time series analysis.
The variables are:
ExRate: represents the exchange rate
GoPri: gold price in India
GDPGr: Gross Domestic Product (GDP) growth rate of India
23
VIX: Volatility Index of India
T test:
This is a probability test done to test the significance of the coefficient of an independent
variable on the dependent variable. Usually, the test is done at 5% level of significance
with z value as +1.96 / -1.96
If the t statistics value which is the difference between the estimator
̂ (𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑠𝑎𝑚𝑝𝑙𝑒 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟) and  (population parameter) of a particular
independent variable divided by the standard error of ̂ i.e., se (̂ ) [ in this case as 𝐻0
takes  as zero the formula resembles (1)] lies between the acceptance region i.e., -1.96
and +1.96 then we accept 𝐻0 and state that the particular independent variable has no
effect on the dependent variable or it is insignificant ; whereas if the value of t statistic
is greater than 1.96 or less than -1.96 than we reject 𝐻0 and accept 𝐻𝑎 that says that the
independent variable under consideration significantly effects the dependent
variable.
1) The causal effect of Exchange rate on Nifty index can be tested as follows:
24
𝐻0: 𝛽1 = 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 of Exchange rate on Nifty index 𝑖𝑠 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦
𝑖𝑛𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 i.e., there is no effect of Exchange rate on Nifty index or stock market)
𝐻𝑎: 𝛽1< 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 Exchange rate on Nifty index 𝑖𝑠 negative 𝑎𝑛𝑑
𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)
The t test statistic is given by:
------------- (1)
Here ̂ stands for ̂ 1
And se stands for standard error of estimator of 1 i.e., ̂ 1
Depending on which coefficient we are testing the  will change from 1, 2, 3, 4
Similarly:
2) The causal effect of gold price on Nifty index can be tested as follows:
𝐻0: 𝛽2 = 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 of gold price on Nifty index 𝑖𝑠 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑖𝑛𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡
i.e., there is no effect of gold price on Nifty index or stock market)
𝐻𝑎: 𝛽2< 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 gold price on Nifty index 𝑖𝑠 negative 𝑎𝑛𝑑
𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)
The t test statistic is given by:
Here ̂ stands for ̂ 2
And se stands for standard error of estimator of 2 i.e., ̂ 2
3) The causal effect of GDP growth rate on Nifty index can be tested as follows:
25
𝐻0: 𝛽3 = 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 of GDP growth rate on Nifty index 𝑖𝑠 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦
𝑖𝑛𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 i.e., there is no effect of GDP growth rate on Nifty index or stock market)
𝐻𝑎: 𝛽3> 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 GDP growth rate on Nifty index 𝑖𝑠 positive 𝑎𝑛𝑑
𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)
The t test statistic is given by:
Here ̂ stands for ̂ 3
And se stands for standard error of estimator of 3 i.e., ̂ 3
4) The causal effect of Volatility index (VIX) on Nifty index can be tested as follows:
𝐻0: 𝛽4 = 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 of Volatility index (VIX) on Nifty index 𝑖𝑠 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦
𝑖𝑛𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 i.e., there is no effect of Volatility index (VIX) on Nifty index or stock
market)
𝐻𝑎: 𝛽4 < 0 (𝐶𝑎𝑢𝑠𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 Volatility index (VIX) on Nifty index 𝑖𝑠 negative 𝑎𝑛𝑑
𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)
The test statistic is given by:
Here ̂ stands for ̂ 4
And se stands for standard error of estimator of 4 i.e., ̂ 4
With the t statistics we conclude about the significance and determine the fit of the
model for examining our variables.
Research Question 3
26
Correlation matrix results are used to find the relationship between dependent variable and
independent variables. This further examines the foundation of our theory that we use to base our
regression on.
Research Question 4
Then we employ ARDL (Autoregressive Distributed Lag (ARDL) for cointegration
Research Question 4
Then we use Gregory Hansen test for seeing structural break during covid in our model during the
time period under consideration.
3.2 Methodology
3.2.1 Augmented Dickey Fuller Unit root test
The meaning of stationary series is
1) Constant mean
2) Constant variance
3) Autocovariance should not depend on time
Dickey and Fuller test:
Yt = bYt-1 + e
 Here if b greater than 1 it means series will be explosive
 b = 1 it means every lag value reflects in current value and the effect will be
consistent. This shows relationship exists between current and lag values will remain
throughout the sample. This means that the series is non stationary. The series has a
unit root (b =1). when we test stationary properties of any series the test is called unit
root test.
 b <1 the effect of lagged values will die out and relationship between current and
lagged values will be no more This shows the series is stationary
Yt = bYt-1 + e
Subtract Yt-1 on both sides
Yt – Yt-1 = (b – 1) Yt-1+ e
D(Yt) = (b – 1) Yt-1+ e
D(Yt) = C*Yt-1+ e where C = b -1
D(Yt) = a + W*t + C*Yt-1+ e with trend and intercept
27
D(Yt) = a + W*t + C*Yt-1 + d(Yt-1) + e Augmented Dickey Fuller (ADF)
Unit root test of ADF, Ho: Series is not stationary
28
If p value is greater than 0.05 it means accept Ho which shows series is non
stationary i(1)
If p value is less than 0.05 it means reject Ho which shows series is stationary
[i(0)]
Variables
Dep Variable Nifty
Exchange Rate
GDP growth rate
Volatility Index
Gold Price
Augmented Dickey Fuller Test
Level
1st Difference
Constant and trend
Constant and Trend
-1.74
-4.27
(0.709)
(0.0106)
-2.54
-3.77
(0.305)
(0.032)
-3.99
Not required as it is
stationary [i(0)]
(0.019)
-3.06
-5.96
(0.131)
(0.0002)
-2.36
-3.85
(0.388)
(0.0275)
We check the p values for each variable to conclude about stationarity
1) Dependent variable Nifty – the p value of nifty at level is greater than 0.05
hence we accept Ho That is it is non stationary
Now we need to test at 1st difference for non-stationarity
The p value at first difference < 0.05 therefore Ho is rejected proving nonstationarity at first difference therefore the series is i(1) and non-stationary
2) Exchange Rate- the p value of Exchange rate at level is greater than 0.05
hence, we accept Ho That is it is non stationary
Now we need to test at 1st difference for non-stationarity
The p value at first difference < 0.05 therefore Ho is rejected proving nonstationarity at first difference therefore the series is i(1) and non-stationary
3) GDP growth rate – the p value of GDP growth rate at level is less than 0.05
hence we reject the Ho and conclude that the variable is stationary i(0)
29
4) Volatility Index - the p value of Volatility Index at level is greater than 0.05
hence we accept Ho That is it is non stationary
Now we need to test at 1st difference for non-stationarity
The p value at first difference < 0.05 therefore Ho is rejected proving nonstationarity at first difference therefore the series is i(1) and non-stationary
5) Gold Price – the p value of gold price at level is greater than 0.05 hence we
accept Ho That is it is non stationary
Now we need to test at 1st difference for non-stationarity
The p value at first difference < 0.05 therefore Ho is rejected proving nonstationarity at first difference therefore the series is i(1) and non-stationary
Hence, we have tested for stationarity and concluded that
1) Dep Variable Nifty is i (1) non stationary at first difference
2) Exchange Rate is i (1) non stationary at first difference
3) GDP growth rate is i (0) and stationary
4) Volatility Index is i (1) and non-stationary at first difference
5) Gold price is i (1) and non-stationary at first difference
3.2.2 OLS and significance
Now we move to OLS and that can be done when all the variables are stationary
For that we do first differencing of the non-stationary i(1) variables making it
stationary and then check for significance.
In stata we do first differencing by generating variables in the form
Variable_d = d.variable
We do not change GDP growth rate as it is already stationary
Then we do dicky fuller tests to ensure all the variables have been made stationary
Mac kinnon approximate p-value for Z(t) (abbreviated as MK value) to check for
stationarity:
Variable name
MK value
30
0.0009
0.0040
0.0024
0.0000
0.0039
DepVariablenifty_d
ExchangeRate_d
GoldPrice_d
Volatilityindexvix_d
GDPGrowthrate
Here we have all the values to be less than 0.05 so we reject Ho and conclude that
all variables have been converted to i(0) (stationary) [except GDP growth rate
already i(0)] now we can run ols and see significance.
When we run the regression the OLS results are as follows:
DepVariablenifty_d
ExchangeRate_d
GoldPrice_d
Volitalityindexvix_d
gdpgrowthrate
_cons
Coeff
-185.72
-0.160
-85.31
18.4834
699.733
Std Error
74.082
0.064
24.612
19.644
224.723
t
-2.51
-2.50
-3.47
-2.07
3.11
P > |t|
0.019
0.019
0.002
0.012
0.004
R-squared 0.5120
The above table shows that the independent variables are significant as the t values
lie in the rejection region that is < -1.96 and >1.96 (at 5% level of significance)
and the p values are less than 0.05 and hence statistically significant. Therefore,
the model has great fit as the independent variables significantly explain the
variation in the dependent variable Nifty supported by both p values as well as t
statistics.
3.2.3 Descriptive statistics of the regression
31
mean
median
maximum
minimum
Standard
deviation
skewness
Kurtosis
Jarque Bera
Probability
Observations
Dep
Variable
Nifty
11609.89
10719.28
18291.95
7429.667
3274.016
Exchange GDP
growth
Rate
rate
Gold
Price
Volatility
Index
70.23688
70.29000
82.20000
62.24000
5.027035
7.933750
9.750000
20.10000
-22.2900
7.161505
37044.35
31854.33
52641.33
25475.00
9633.946
17.93990
17.07000
34.99667
11.45000
5.114742
0.755520
2.295881
3.705367
0.156816
32
0.406157
2.428843
1.314765
0.518206
32
-2.454917
11.25618
123.0279
0.000000
32
0.425534
1.484676
4.027363
0.133496
32
1.681703
6.021641
27.25709
0.000001
32
Interpretation:
The above table summarizes the data of all the variables and gives valuable
insights.
Mean
a) The mean value of Dependent Variable Nifty is 11.609.89 (rupees)
representing 50 sectors of Indian economy during 2015-2022
b) The mean value of Exchange rate is 70.23688 rupees per dollar during 20152022.
c) The mean value of GDP growth rate 7.933750 % during 2015-2022.
d) The mean value of Gold Price is 37044.35 rupees per 10 g (24 carat)
e) The mean value of Volatility index is 17.93990 vix points
Standard Deviation
A standard deviation is a measure of how dispersed (spread out) the data is in
relation to the mean.
The standard deviation values of Dependent Variable Nifty and Gold Price are
huge compared to other variables which represents that the values for these
variables are spread out farther from the mean whereas when the standard
deviation values are less the values are clustered closer to the mean.
It signifies how spread out the data of the variables is. This is expected as the
32
values of Nifty and Gold price are bigger figures and hence more variation
especially due to the existence of a volatile period.
Skewness
Skewness can be quantified as the extent to which a given distribution
varies from a normal distribution. ( the symmetrical bell shape)
As per the above diagram we classify the variables as positively and negatively
skewed.
a) Nifty is positively skewed as the value is between 0.5 and 1
b) Exchange rate is nearly symmetrical as the value is between -0.5 and 0.5
c) GDP growth rate is negatively skewed since the value is less than -1
d) gold price is nearly symmetrical as the value is between -0.5 and 0.5
e) Volatility index VIX is positively skewed as the value is greater than 1
Kurtosis
Kurtosis is the measure of how often the outliers occur or the tailedness of the
distribution. When the kurtosis values are less than 3 then the distribution is
platykurtic distribution. The dependent variable Nifty, Exchange rate and gold
price are platykurtic. When the kurtosis values are greater than 3 then the
distribution is leptokurtic distribution. GDP growth rate and volatility index VIX
are leptokurtic distributions.
These results are summarized as in the below figure.
33
3.2.4 Correlation matrix
Dep
Exchange GDP
Variable Rate
growth
Nifty
rate
1.000
-0.826
0.169
Gold
price
Volatility
index
-0.844
-0.134
Exchange
Rate
-0.826
1.000
-0.4007
0.913
0.424
GDP
growth
rate
0.169
-0.400
1.000
-0.358
-0.616
Gold price -0.844
0.913
-0.358
1.000
0.482
-0.134
0.424
-0.616
0.482
1.000
Dep
Variable
Nifty
Volatility
index
Explanation: The relationship observed through our regression results is solidified
through the correlation matrix which shows the extent of linear relationship
between the dependent and independent variables. The diagonal has the value 1
34
because the relationship of the variable with itself is perfect positive correlation
a) As expected, Exchange rate has a negative correlation with nifty index as we
stated in the theory, the correlation is high and they are significantly related [ As
the value is greater than 0.8]
b) gold price also effects the nifty index negatively, and again they are significantly
related to each other [ As the value is greater than 0.8]
c) GDP growth rate has comparatively less relation to Nifty index according to the
value this could be due to less accuracy of data, as the GDP growth rates of recent
years are less credible as work on data collection on such rates take time.
d) Volatility index is negatively correlated to the Nifty index, the reason for which
was given in the theory supporting the link between independent and dependent
variables.
e) Among the independent variables there is high correlation between exchange
rate and gold price, which is a cause of concern. However, we do expect some kind
of correlation between the independent variables as the macroeconomic variables
are usually closely linked.
3.2.5 Autoregressive Distributed Lag (ARDL) cointegration technique
Now we come to the most important test, which is done to test for cointegration.
It is a technique used to find a possible correlation between time series processes
in the long term. This test is usually applied when some of the variables in the
regression are i(1) and some are i(0). In our case there are 4 i(1) variables and 1
i(0) variables
So, we check for cointegration between these variables to conclude long run
relationship.
Autoregressive (AR) In an AR model, the independent variables are all lagged
variables dependent variable. There is no other independent variable.
For e.g., Ct = α + 1Ct-1 + 2Ct-2 + μt
Where dependent variable is
Ct
consumption of current period. Whereas
independent variables Ct-1 shows consumption of one period ago and Ct-2 shows
consumption of two periods ago.
Distributed Lag: (DL)
When an independent variable appears in a regression more than once, with
different time lags, it is a distributed lag model. It has this name because the
35
influence of the independent variable is spread out or distributed across several
time periods.
Ct = α + 1INCt + 2INCt-1 + 3INCt-2 + et
Where lNC is income and C is consumption. The influence of income on
consumption spreads across three periods: t (current period),
t-1(1 period ago) and t-2 (2 periods ago).
By combining AR and DL model we obtain ARDL model as follows:
Ct = α + 1Ct-1 + 2Ct-2 + 3INCt + 4INCt-1 + 5INCt-2 + εt
ARDL cointegration is used when considered variables have different order of
integration that is some variables are stationary at level and some are stationary
at first difference. For this purpose, ARDL cointegration or bound test is used.
1) If F-stats is greater than value of upper bound, this shows there is cointegration.
2) If F-stats is in between the value of upper bound and lower bound, this shows
the result is inconclusive.
3) If F-stats is less than value of lower bound, this shows there is no cointegration.
Results:
First, we check the stationarity of all the variables with unit root test if some are
i(0) [stationary at level] and some are i(1) [stationary at first difference] like we
found out before, we go ahead towards ARDL cointegration method.
Variable
Coefficient
Std.
Error
t-Statistic
DEP_VARIABLE_NIFTY (-1)
DEP_VARIABLE_NIFTY (-2)
DEP_VARIABLE_NIFTY (-3)
DEP_VARIABLE_NIFTY (-4)
EXCHANGE_RATE
EXCHANGE_RATE (-1)
EXCHANGE_RATE (-2)
EXCHANGE_RATE (-3)
EXCHANGE_RATE (-4)
GDP_GROWTH_RATE
GDP_GROWTH_RATE (-1)
GDP_GROWTH_RATE (-2)
GDP_GROWTH_RATE (-3)
GDP_GROWTH_RATE (-4)
GOLD_PRICE
0.292315
-0.736771
1.704732
-2.046386
400.9051
-243.6791
417.5929
326.9174
-227.4637
237.5693
-22.54771
95.44509
17.38540
-80.86297
0.598507
0.318711
0.438447
0.620777
0.634156
189.0936
167.8131
176.9645
178.0785
148.1200
91.37561
31.81942
42.77149
39.71437
27.77807
0.229790
0.917181
-1.680410
2.746128
-3.226947
2.120141
-1.452087
2.359755
1.835805
-1.535672
2.599921
-0.708615
2.231512
0.437761
-2.911036
2.604583
36
Prob.*
0.4109
0.1682
0.0516
0.0321
0.1013
0.2201
0.0777
0.1403
0.1994
0.0601
0.5177
0.0895
0.6842
0.0436
0.0598
GOLD_PRICE (-1)
GOLD_PRICE (-2)
GOLD_PRICE (-3)
VOLITALITY_INDEX__VIX_
VOLITALITY_INDEX__VIX_ (1)
VOLITALITY_INDEX__VIX_ (2)
VOLITALITY_INDEX__VIX_ (3)
VOLITALITY_INDEX__VIX_ (4)
C
-0.215754
-0.171656
0.272786
119.7882
-345.1487
0.145953
0.128121
0.123753
109.9653
109.3698
-1.478249
-1.339801
2.204273
1.089328
-3.155796
0.2134
0.2514
0.0922
0.3372
0.0343
-151.5136
71.16805
-2.128955
0.1003
-11.55442
51.62252
-0.223825
0.8339
-326.7888
135.2911
-2.415449
0.0731
-34863.45
15962.23
-2.184122
0.0943
R-squared
0.997644
Mean dependent var
12083.59
Adjusted R-squared
0.984095
S.D. dependent var
3230.278
S.E. of regression
407.3866
Akaike info criterion
14.62578
663855.3
Sum squared resid
Schwarz criterion
15.76767
-180.7609
Hannan-Quinn criter
14.97486
F-statistic
73.63390
Durbin-Watson stat
2.069111
Prob(F-statistic)
0.000393
Log likelihood
We obtain the ARDL model as in the example we explained along with all the
necessary details like R2 , F statistics etc.
Now we do the main test with the help of the F statistic
Which is the long run form and Bound test for cointegration and long run
coefficient.
Levels Equation
Case 2: Restricted Constant and No Trend
Variable
Coefficient
Std.
Error
t-Statistic
Prob.
EXCHANGE_RATE
-377.5090
4.674715
0.0095
GDP_GROWTH_RAT
E
GOLD_PRICE
138.2833
80.7555
1
68.7633
4
0.04428
5
55.5511
7
5122.27
7
2.011003
0.0047
6.117577
0.0036
-7.208361
0.0020
-3.810650
0.0189
VOLITALITY_INDEX_
_VIX_
C
-0.270914
-400.4329
-19519.21
EC = DEP_VARIABLE_NIFTY - (377.5090*EXCHANGE_RATE +
138.2833
*GDP_GROWTH_RATE + 0.2709*GOLD_PRICE -400.4329
*VOLITALITY_INDEX__VIX_ - 19519.2064)
37
F-Bounds Test
Test Statistic
F-statistic
k
Actual Sample Size
Null Hypothesis: No levels
relationship
Value
2.364273
4
Signif.
I (0)
10%
5%
2.5%
1%
Asymptotic
: n=1000
2.2
2.56
2.88
3.29
3.09
3.49
3.87
4.37
10%
5%
1%
Finite
Sample:
n=35
2.46
2.947
4.093
3.46
4.088
5.532
10%
5%
1%
Finite
Sample:
n=30
2.525
3.058
4.28
3.56
4.223
5.84
28
I (1)
These are the results from long run form and Bound test, we mainly look at the f
statistic highlighted in bold for concluding whether cointegration is present or
not. It is 2.36427
In the table the value for lower bounds is given by:
2.2, 2.56, 2.88, 3.29
Whereas those from upper bound is given by :
3.09, 3.49, 3.87, 4.37
At different significant levels.
At 1% significance level we have f statistic is less than the lower bound value
which is 3.29
Therefore, as mentioned before when : If F-stats is less than value of lower bound,
this shows there is no cointegration.
Now we go to error correction form for short run coefficients
38
ECM Regression
Case 2: Restricted Constant and No Trend
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(DEP_VARIABLE_NIFTY (-1))
1.078426
0.210941
5.112460
0.0069
D(DEP_VARIABLE_NIFTY (-2))
0.341654
0.161850
2.110928
0.1024
D(DEP_VARIABLE_NIFTY (-3))
2.046386
0.322653
6.342373
0.0032
D(EXCHANGE_RATE)
400.9051
103.4375
3.875818
0.0179
D(EXCHANGE_RATE (-1))
-517.0466
110.1367
-4.694591
0.0093
D(EXCHANGE_RATE (-2))
-99.45370
60.70154
-1.638405
0.1767
D(EXCHANGE_RATE (-3))
227.4637
62.77120
3.623696
0.0223
D(GDP_GROWTH_RATE)
237.5693
38.99766
6.091887
0.0037
D(GDP_GROWTH_RATE (-1))
-31.96752
14.45142
-2.212067
0.0914
D(GDP_GROWTH_RATE (-2))
63.47757
14.43682
4.396923
0.0117
D(GDP_GROWTH_RATE (-3))
80.86297
16.39213
4.933035
0.0079
D(GOLD_PRICE)
0.598507
0.110537
5.414534
0.0056
D(GOLD_PRICE (-1))
-0.101130
0.055227
-1.831178
0.1410
D(GOLD_PRICE (-2))
-0.272786
0.077811
-3.505756
0.0248
D(VOLITALITY_INDEX__VIX_)
119.7882
42.86806
2.794347
0.0491
D(VOLITALITY_INDEX__VIX_ (-1))
489.8568
103.0408
4.754010
0.0089
D(VOLITALITY_INDEX__VIX_ (-2))
338.3433
69.79445
4.847710
0.0084
D(VOLITALITY_INDEX__VIX_ (-3))
326.7888
65.58499
4.982677
0.0076
CointEq(-1)*
-1.786110
0.316150
-5.649574
0.0048
R-squared
0.957793
Mean dependent var
368.1958
Adjusted R-squared
0.873378
S.D. dependent var
763.2397
S.E. of regression
271.5911
Akaike info criterion
14.26863
Sum squared resid
663855.3
Schwarz criterion
15.17263
Hannan-Quinn criter.
14.54500
Log likelihood
Durbin-Watson stat
-180.7609
2.069111
The ones that are highlighted are in bold are the short run coefficients
The long run coefficient is not applicable as the there is no cointegration so
CointEq(-1)* is not applicable here.
However, if you cannot find cointegration with the ARDL there may be a
significant break in the series which we already advocate that is due to covid. We
therefore run cointegration tests that consider breaks i.e., Gregory and Hansen
cointegration test.
3.2.6 Gregory Hansen cointegration test
39
Now we employ the test for Gregory Hansen to test for structural break and
cointegration due to covid which started having its effects in 2019. So, we see if
there is a sharp shift in the trend of the dependent variable that is Nifty
representing the stock market.
Structural break is when an event has affected the trend of a particular series
especially time series. That is when there is a visible difference between the past
and future movements in a particular series.
If the variables are integrated of different orders, the bound tests (long run form
and bound test in ARDL) is used.
With break in any of the series, bound test will yield inconsistent results. Hence,
we use Gregory and Hansen test designed for cointegration testing when
controlling for structural breaks. The authors Gregory and Hansen advocate an
approach that involves testing the null hypothesis of no cointegration against an
alternative of cointegration with a single break in an unknown date based on
extensions of Augmented Dickey Fuller (ADF), Zα , Zt test types.
The test is as follows:
Null hypothesis H0 : no cointegration at the break point
Alternative hypothesis HA : there is cointegration at the break point
Decision: Reject the null hypothesis if the absolute value of the Z t statistic is
higher than the 5% critical value, otherwise do not reject.
If the Null hypothesis is rejected it implies that the linear combination of the
variables exhibits stable properties in the long run with structural break.
First, we use quarters as the time variation and use the time variation command
in stata which describes the data as varying from 2015 Q1 to 2022 Q4 and the
unit of time as quarters
Then we plot the dependent variable through a trend line:
40
When we plot the dependent variable Nifty, we get the above graph
Structural break is observed, in the steep turn of data (representing an inverted
triangle) between 2019 Q1 and 2021 Q1 which we will confirm with the test
further.
Now we use the test to see the break in constant and slope
We run the test with the break at regime and lag method (bic). The BIC is a wellknown general approach to model selection that favours more parsimonious
models [ A parsimonious model is a model that accomplishes the desired level
of explanation or prediction with as few predictor variables as possible] over
more complex models.
Now the test results are obtained in the table below:
41
Gregory Hansen test for cointegration with regime shifts
ADF
Zt
Za
Test
Statistic
Breakpoint Date
Asymptotic critical values
-6.67
-6.66
-37.66
19
19
19
1%
-6.92
-6.92
-90.35
2019q3
2019q3
2019q3
5%
-6.41
-6.41
78.52
10%
-6.17
-6.17
-75.56
The test was performed for mainly regime to test for change or break in constant
and slope
Both the ADF and Zt statistic are greater than the values at 5% critical value ( 6.41) it means the null hypothesis is rejected for no cointegration at break point
and the Ha is accepted for there is cointegration among the variables at the break
point.
So, break is evident in 2019 q3 so even when covid was not officially announce ,
the first initial phase was enough to show a structural break in the model ( ADF
and Zt statistic both are significant at the 5% level)
Hence, we say that there is a structural break in the model and cointegration
among the variables in the presence of Covid -19.
42
CHAPTER 4
CONCLUSION
In conclusion, the main aim of the study was to establish the relationship of the 4
macroeconomic variables :
1) Exchange rate
2) Gold price
3) Volatility Index (VIX)
4) GDP growth rate
With the dependent variable The NIFTY 50 index which is a benchmark Indian
stock market index that signifies :
The weighted average of 50 of the largest Indian companies listed on the National
Stock Exchange. We based this study on the Indian stock market one of the
integral parts of financial markets for any economy be it developing or developed.
The Nifty index is a comprehensive indicator of the fluctuations/ volatilities in
the stock market of India since it is a national indicator. We chose only these
indicators due to the following reasons:
1) Exchange rate: This is one of the variables that determine the imports exports
in short the trade balance of a country which can considerably effect the foreign
exchange reserves of a country which is the most crucial form of earning and
considerably determine the growth trajectory of a country, it acts as the push to
encourage further Foreign Direct Investments (FDI) , Foreign Institutional
Investments (FII’s) into the country which can make the country an attractive hub
for the inflow of investments from abroad. Instead of just being a mere
macroeconomic indicator it is also encompasses the relationship of a Domestic
economy with the global market. Its fluctuations can determine the dependency
of a country with its foreign partners. For example: if there is a trade deficit it
means the imports are greater than exports therefore the country does rely on
other countries for its sustenance. Whereas if there is a trade surplus the exports
are greater than imports which means the country has an upper hand and is not
completely reliant on other countries which is a positive sign. Hence it is a crucial
indicator to determine stock index as well since depreciation can lead to increase
in import prices and hence increase the inflation level affecting the stock market
returns
43
2) Gold Price : Again, its relevance is undisputable since it is an alternative form
of asset to the stock market. If gold prices increase it will become an attractive
form of asset as compared to the stock market. So, its value can very well
determine how many people or investors would invest in the stock market and
hence also determine the stock market trends.
3) Volatility Index (VIX): Again, highly indicative of what will be the investor
sentiment towards the stock market. If the volatility is high then the investors
would be critical of investing in the stock market as the returns could be
fluctuating leading to high losses in the long run. It also reflects the nature of the
stock market at a period maybe due to global fluctuations, other trends prevalent,
conflicts within the domestic economy etc. So , it can be said that when VIX is
high, Stock indices can come down due to the negative sentiment in the economy
about investment in stock market and the returns
4) GDP growth rate: When the GDP growth rate is high there is positive sentiment
in the economy due to the GDP stats, and therefore theoretically the stock index
should go up. The GDP is the most important macroeconomic indicator of any
economy so its consideration tells us a lot about what trends the stock market will
follow as was discussed in the variable section.
The graphs plotted of the variables, show the trends of dependent as well as
independent variables with time (2015-2022) and the outliers are mostly in the
covid period (when its first cases were announced). First and foremost, we tested
for the stationarity of the variables which was necessary for ensuring if OLS can
be employed or not. The results yielded that exchange rate, gold price, Volatility
Index (VIX) are non-stationary i(1) and GDP growth rate is stationary now, we
can employ OLS but only after converting them to stationary that can be done by
taking first difference. Then we analyse and find that all the four variables
included are significant and have a considerable influence on the dependent
variable. Then we employ ARDL method to see cointegration since some are i(0)
and some are i(1) , however we see no cointegration due to the presence of
structural break that is the nature of the coefficients are changing during covid
and the way they are affecting the dependent variable basically the stock market
is also changing. Then we apply Gregory Hansen cointegration test to check for
cointegration in the presence of structural break. And as we anticipated the
structural break is happening at 2019 q3 (the data is in quarterly format) and
hence we validate that Covid has had a considerable impact on the Indian stock
market (and this was done through analysing the macroeconomic indicators to
make the study more comprehensive.)
44
Conclusion, the study was done to find how the impact of the macroeconomic
variables under consideration change due to Covid 19 pandemic. And the
presence of outliers in the data during that period, The ARDL test not showing
results due to structural break is another evidence to show covid impact. The
Gregory Hansen test is the pivotal point of our study ( also the graph which shows
the necessary break as shown before) which proves cointegration and structural
break as well as gives us the exact point of structural break
Which is 2019 Q3 when the first cases started coming to notice.
This study is essential since it shows how the major macroeconomic indicators of
a country can undergo major shift in their trends once a pandemic or uncertainty
takes over. This means that this topic can be studied and some precautions can be
taken or other mechanisms can be put to place to avoid such contingencies or to
deal with them better. The policy implication is that the macroeconomic
indicators are also connected among themselves. So any volatility can have a
chain effect. Hence policies to stabilise or to keep these indicators in check can
help the Indian stock market or in general the economy to be better prepared to
handle a pandemic/ uncertainty better in the future.
Limitations of the study:
However, it is important to note that the stock market can be affected by many
other events like global recession / boom , their spill over effects can also affect
indices considerably and this has not been considered in our study. Along with
that we must not miss out on the fact that there are many other macroeconomic
indicators that effect the stock market in addition to the variables we have
considered like oil price, interest rate, money supply etc.
45
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