STUDY OF THE IMPACT OF MACRO ECONOMIC VARIABLES AND VOLATILITY OF BANKING SECTORS IN NSE - infokara research
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INFOKARA RESEARCH ISSN NO: 1021-9056
STUDY OF THE IMPACT OF MACRO
ECONOMIC VARIABLES AND VOLATILITY OF
BANKING SECTORS IN NSE
Gayathry1, Dr. J.samuelCaeser Pickens 2
1
Research Scholar, Dr.SNS Rajalakshmi College of Arts and Science, Autonomous,
Coimbatore – 49.
2
Asst. Professor and Head, Department of Commerce with IT, Dr.SNS Rajalakshmi College
of Arts and Science, Autonomous, Coimbatore - 49.
ABSTRACT
In this present scenario of the world financial market there seems to be stock
exchange on almost every country expressing the financial health of the respective economy.
This study investigates the volatility by using GARCH model along with the impact on
scheduled macro-economic variables with selected banks sectors listed in NSE.Considered
for the study arethe four macro-economic variables are Interest Rate(IR),Foreign exchange
rate(USD-INR),Crude Oil Price(CP),Gold Price(GP).The result exhibit the significant impact
of macroeconomics variables on NSE. Main observational of this study are to find volatility
and effects of macro-economic variables in indices. Hypothesis testing on price volatility and
impact of macro-economic variables. Empirical study period was from April 2016 to March
2018. Hence it is recommended for the investors to continuously have a close watch on the
frequent changes, based on the return on investing. So effects can be attained by making
correct investment at gain profit.
Keywords: Stock Exchange, Banking Sectors, , Macroeconomic Variables. Volatility.
INTRODUCTION
The stock market is one of the most important sources for companies to raise money.
Indian stock market has developed in term of number of stock exchange and other
intermediaries. The number of listed stocks, trading volume, market capitalisation, turnover
of the stock exchange, investor involvement and price indices. Macro economy is the study of
the economy as a whole, and the variables that control the macro economy. The study of
government policy meant to control and stabilizes the economy over time, that is to reduce
fluctuations in the economy. Macro Economics treats the components of the economy as one
unit, as one aggregate, that is looks for relationships between the various components. These
variables are indicators or main signposts signalling the current trends in the economy it
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understands the major variables that determine the current behaviour of the macro economy
so government must understand the forces of economic growth, why and when recession or
inflation occur, and anticipate these trends as well as what determine the policy which will
be most suitable for curing whatever its economy. After extensive survey with macro-
economic variables we arrived at a conclusion that the macro variables mentioned play major
role in the economy. In this study the researcher has particularly focused on banking sectors
listed in NSE because total market fluctuations are determined by performance of NSE.
Markets increase the volatility around announcement period with an anticipation that affect
the market and diminish the investor expectations.
REVIEW OF LITERATURE
Raman Preet Singh(2011), has done a Study of The Impact of Macro Economic
Variable & Their Role as an Indicators for the S&P CNX Nifty, the objective of this paper is
to study the role of these seven macroeconomic variable in relation to Nifty, how these
variables can be used to predict the movement of the Nifty Index and to which category of
indicator these macro-economic variables correlate The Granger causality test with the data
ranging from 2002-2011 has been used to prove that there is no cause and effect relationship.
It has been found that with the help of this method we can predict the future.
N.Sangeetha(2013), to analyse the Impact of Macro economic Factors on Share Price
Movements of Trading Sectors, the objective of the study is to determine the risk return
relationship of the stock, study the relationship between the macro economic factors and the
share price movements and also to analyse the impact of the factors on the performance of the
companies’ stocks. The analysis is done with 30 companies taken from 6 sectors (5
companies from each sector). The data are based on secondary data is that used in this study
and was collected from national stock exchange website and various journals, reports,
publications and historical documents. It has been founded and interpreted that the
performance of Banking sector will be good and the Telecommunication sector will be the
low performing sector. It has been suggested that the investor should have knowledge of
exchanging the stocks during the change in macroeconomic factor. Banking sector is high
performing sector in which investors can invest in future.
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STATEMENT OF THE PROBLEM
Indian stock market overcame many changes has been built based on the policies that
were made by the government and other upper hands. The decreasing share price have been a
equivalent problems are overlooked by each investors. Similarly, sector is designate as an
important factor which makes an changes in the economy and it cross over the gap between
stock market and investors at tonnes. Volatility specify that the degree of variations in the
share price which is purposive during the investment process. High volatility results to more
risk. Although there is change is macroeconomic variable that will affect various sectors. In
this study it has been analysed to know about the extent macroeconomic variable that has an
effect on share market and the reason for the unstable fluctuations that appear in various
banking sectors.
OBJECTIVE OF THE STUDY
To investigate the stock price volatility of selected banks listed in NSE.
To find the effect of macroeconomic variables on selected banks listed in NSE.
HYPOTHESES OF THE STUDY
H0: There is no significant change on stock price volatility on selected banks in NSE
H0: There is no significant effect on macroeconomic variable on selected banks in NSE
RESEARCH METHODOLOGY OF THE STUDY
This study uses analytical research because we utilise the facts and information
already available, and analyse these to make a critical evaluation of the material.
Data Source
The data used in for the study is purely secondary in nature with the monthly closing
price of the banking indices in NSE and monthly data of macroeconomic variables.
Period of Study
The monthly closing prices have been collected from the official website of National
Stock Exchange for a period of five year from April 1st 2016 to 30th March 2018.
Macroeconomic monthly data collected fromworld bank from yahoo finance.
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Tools used
Descriptive Analysis
Augmented Dickey Fuller Test
GARCH Model
Multiple Regression
Methods of Sampling
Table1: Description of selected Macroeconomic variables
S.NO VARIBALES FREQUENCY OF ANNOUNCEMENT
1 Inflation Monthly
2 Gold Price Monthly
3 Exchange Rate Monthly
4 Crude oil Monthly
Table 2: Description of selected banking sectors listed in NSE
S.No Bank Name Last % 52 week 52 week Market
Price Change high Low Capitalization
(Rs.Cr)
1 State Bank of India 271.10 1.01 373.70 247.65 241,946.22
2 Bank of Baroda 93.45 3.20 156.25 89.75 35,947.67
3 Punjab National Bank 63.55 3.42 99.90 58.65 29,258.72
4 HDFC 2,163.25 0.48 2,502.90 1884.40 591,490.98
5 Kotak Mahindra Bank 1472.50 0.32 1,555.45 1,002.30 281,185.14
6 ICICI 395.75 0.85 443.85 294.80 255,460.39
SIGNIFICANCE OF THE STUDY
The study highlights the measure of stock price volatility with dynamic influence that
occurs on and around the economic market. It is also important to realize the macroeconomic
variable which influence stock price, effect is also been analysed to know its impact among
the share price of the selected banks with macroeconomic variable. This study will also help
the investor to make a better investment process and render the profit as well.
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Summary of
statistics SBI BOB PNB HDFC KMB ICICI
Mean 266.5833 159.2813 137.2167 1524.869 892.6750 267.3167
Median 264.1750 161.1500 139.4250 1494.525 887.0750 252.5200
Maximum 320.3500 187.5500 197.1500 2006.350 1108.950 352.9500
Minimum 189.0000 137.6000 79.0000 1132.350 717.1500 215.4100
Standard 34.46204 12.69357 29.70983 294.4853 128.7070 37.22972
Deviation
Skewness -0.370544 -0.002725 -0.139924 0.121082 0.122524 0.424696
Kurtosis 2.655096 2.645621 2.516236 1.400913 1.518589 2.209721
JarqueBera 0.668170 0.125614 0.312342 2.615722 2.254627 1.346957
Probability 0.715993 0.939125 0.855413 0.270398 0.323902 0.509932
Number of 24 24 24 24 24 24
Observation
Table 3 : Descriptive Analysis for selected banking sectors listed under NSE
(Source: Computed from yahoo finance)
Table 3. Represents the descriptive statistics on share price of selected banking sector
for the period of 1st April 2016 to 31sr march 2018 on monthly data. It reveals that the mean
value of share price is 1524.869 on HDFC bank and its standard deviation is 294.4853 with
high value it also implies that there is high degree of variability due to less deviation. It
means the mean value is distribution normally. The distribution of share price of SBI, BOB,
PNB is negatively skewned (-0.370),(0.002)and (-0.139) that indicates the distribution
towards the left tailed. Kurtosis is distributed is platykurtic. The p-value of JarqueBera (J-B)
test statistics of share price (0.71),(0.93),(0.855),(0.270),(0.323), and (0.509) is not
significant at 5 percent with probability not normally distributed share price. The H0 null
hypothesis is accepted.
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Table 4 : The Augmented Dickey Fuller Test of Selected Banking Sectors
Banking Closing price of Banking Sectors
Sector Level 1st difference 2nd difference
t-statistic probability t-statistic probability t-statistic probability
SBI -2.843 0.067 -4.484 0.002 -6.434 0.000
BOB -2.563 0.114 -5.364 0.003 -5.713 0.000
PNB -2.994 0.054 -2.646 0.101 -7.961 0.000
HDFC -0.819 0.794 -4.103 0.004 -8.142 0.000
KMB -1.026 0.725 -5.246 0.004 -8.735 0.000
ICICI -1.691 0.422 -4.919 0.008 -6.437 0.000
(Source: Computed from NSE)
Table 4 reveals the unit toot test applied to determine the order of integration among
the time series data of select banking sector in NSE. The Augmented Dickey fuller test is
used at level , first difference and in second difference under the assumption intercept.
According to the results banking sectors like SBI, BOB,PNB, HDFC,KMB and ICICI are not
stationary on level and first difference but it has to become stationary when it was on second
difference. The degree of integration of series I(1)
Table 5: The Augmented Dickey Fuller Test of Selected Macroeconomic Variables
Banking Price of Macroeconomic Variables
Sector Level 1st difference 2nd difference
t-statistic Probability t-statistic Probability t-statistic probability
INFLATION 1.546 0.998 -4.482 0.024 -7.196 0.000
GOLD PRICE -1.581 0.347 -5.545 0.002 -9.216 0.000
CPI -1.306 0.608 -4.049 0.005 -5.385 0.000
EXCHANGE -1.896 0.327 -2.656 0.097 -4.926 0.000
RATE
CRUDE OIL -4.412 0.558 -4.520 0.029 -4.153 0.000
(source: computed from world Bank)
Table 5 reveals the unit toot test applied to determine the order of integration among
the time series data of select banking sector in NSE. The Augmented Dickey fuller test is
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used at level , first difference and in second difference under the assumption intercept.
According to the results banking sectors like INFLATION, GOLD PRICE, EXCHANGE
PRICE CPI and crude oil are not stationary on level and first difference but it has to become
stationary when it was on second difference. The degree of integration of series I(1)
TGARCH Model For SBI Bank:
Table 6 : Results of TGARCH estimates of market volatility over the period form 2016-
2018 for State Bank of India
Variable Coefficient St. Error z-Statistic Probability
Constant 5.616659 0.021226 264.6122 0.0000
Variable Equation
Constant 0.003077 0.005065 0.607494 0.5435
RESID(-1)^2 0.285184 0.657733 0.433587 0.6646
GARCH(-1) 0.316115 0.945494 0.334338 0.7381
R-squared -0.089173 Akaike info criterion -1.332318
Adjusted R-squared -0.089173 Schwarz criterion -1.135976
Durbin-Waston stat 0.315732 Hannan-Quinn Criter -1.280228
Source: Compiled and calculated from NSE
Results of TGARCH model stated in table 6 speak about the asymmetric response of
volatility to positive and negative stocks. The volatility modelling captured by Threshold
GARCH model had good news with a impact of ARCH, Here AIC and SIC has a value of -
1.33 and -1.135 which could be a better model to exhibit the volatility. Where p value is less
than the significant value and implies a fluctuation along the volatility. The TGARCH model
best captures the volatility in the progress with its impact. Thereby Durbin Watson stat value
is 0.31 which implies that the model is free from autocorrelation
Table 7 : Results of TGARCH estimates of market volatility over the period form 2016-
2018 for Bank of Baroda
Variable Coefficient St. Error z-Statistic Probability
Constant 5.058485 0.016055 315.0795 0.0000
Variable Equation
Constant 0.005538 0.005748 0.963376 0.3354
RESID(-1)^2 0.560131 0.638847 0.876784 0.3806
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GARCH(-1) -0.454454 0.718963 -0.632096 0.5273
R-squared -0.013483 Akaike info criterion -2.036913
Adjusted R-squared -0.013483 Schwarz criterion -1.840571
Durbin-Waston stat 1.023986 Hannan-Quinn Criter -1.984824
Results of TGARCH model stated in table 7 speak about the asymmetric response of
volatility to positive and negative stocks. The volatility modelling captured by Threshold
GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
value of -2.03 and -1.84 which could be a better model to exhibit the volatility. Where p
value is less than the significant value and implies a fluctuation along the volatility.Thereby
Durbin Watson stat value is 1.02 which implies that the model is free from autocorrelation
Table 8 : Results of TGARCH estimates of market volatility over the period form 2016-
2018 for Punjab National Bank
Variable Coefficient St. Error z-Statistic Probability
Constant 4.961422 0.013903 356.8498 0.0000
Variable Equation
Constant 0.015929 0.009289 1.714761 0.0864
RESID(-1)^2 1.082528 0.735751 1.471325 0.1412
GARCH(-1) -0.308716 0.165226 -1.868442 0.0617
R-squared -0.080641 Akaike info criterion -0.442666
Adjusted R-squared -0.080641 Schwarz criterion -0.246323
Durbin-Waston stat 0.571232 Hannan-Quinn Criter -0.390576
Results of TGARCH model stated in table 8 speak about the asymmetric response of
volatility to positive and negative stocks. The volatility modelling captured by Threshold
GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
value of -0.44 and -0.246 which could be a better model to exhibit the volatility. Where p
value is less than the significant value and implies a fluctuation along the volatility.Thereby
Durbin Watson stat value is 0.57 which implies that the model is free from autocorrelation
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Table 9 : Results of TGARCH estimates of market volatility over the period form 2016-
2018 for HDFC
Variable Coefficient St. Error z-Statistic Probability
Constant 7.273272 0.036000 202.0326 0.0000
Variable Equation
Constant 0.001590 0.002073 0.767211 0.4430
RESID(-1)^2 2.005539 2.168999 0.924638 0.3552
GARCH(-1) -0.507343 1.016350 -0.499182 0.6177
R-squared -0.040599 Akaike info criterion -0.645779
Adjusted R-squared -0.040599 Schwarz criterion -0.449437
Durbin-Waston stat 0.048809 Hannan-Quinn Criter -0.593690
Results of TGARCH model stated in table 9 speak about the asymmetric response of
volatility to positive and negative stocks. The volatility modelling captured by Threshold
GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
value of -0.645 and -0.449 which could be a better model to exhibit the volatility. Where p
value is less than the significant value and implies a fluctuation along the volatility.Thereby
Durbin Watson stat value is 0.04 which implies that the model is free from autocorrelation
Table 10 : Results of TGARCH estimates of market volatility over the period form
2016-2018 for KMB
Variable Coefficient St. Error z-Statistic Probability
Constant 6.679849 0.023021 290.1584 0.0000
Variable Equation
Constant 0.002607 0.002375 1.097745 0.2723
RESID(-1)^2 1.337693 1.466475 0.912183 0.3617
GARCH(-1) -0.241721 0.388962 -0.621450 0.5343
R-squared -0.544199 Akaike info criterion -1.201449
Adjusted R-squared -0.544199 Schwarz criterion -1.005106
Durbin-Waston stat 0.076848 Hannan-Quinn Criter -1.149359
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Results of TGARCH model stated in table10 speak about the asymmetric response of
volatility to positive and negative stocks. The volatility modelling captured by Threshold
GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
value of -1.201 and -1.005 which could be a better model to exhibit the volatility. Where p
value is less than the significant value and implies a fluctuation along the volatility.Thereby
Durbin Watson stat value is 0.07 which implies that the model is free from autocorrelation
Table 11 : Results of TGARCH estimates of market volatility over the period form
2016-2018 for Punjab National Bank
Variable Coefficient St. Error z-Statistic Probability
Constant 5.464606 0.016004 341.4630 0.0000
Variable Equation
Constant 0.002305 0.003763 0.612460 0.5402
RESID(-1)^2 1.308511 0.764658 1.711237 0.0870
GARCH(-1) -0.271121 0.426202 -0.636132 0.5247
R-squared -0.728935 Akaike info criterion -1.165582
Adjusted R-squared -0.728935 Schwarz criterion -0.969240
Durbin-Waston stat 0.145022 Hannan-Quinn Criter -1.113492
Results of TGARCH model stated in table11 speak about the asymmetric response of
volatility to positive and negative stocks. The volatility modelling captured by Threshold
GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
value of -1.165 and -0.969 which could be a better model to exhibit the volatility. Where p
value is less than the significant value and implies a fluctuation along the volatility.Thereby
Durbin Watson stat value is 0.14 which implies that the model is free from autocorrelation
Table 12 : Multiple Regression Analysis of Banking Sector
Banking R R Adj R Std.Error DurninWaston F Sig
Sectors Square Square
SBI .656a .430 .404 26.573 .800 16.590 .001b
BOB .434a .189 -.037 12.907 1.502 .836 .541b
PNB .544a .296 .101 28.155 1.103 10516 .234b
HDFC .992a .985 .981 41.098 1.561 232.491 .000b
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KMB .990a .980 .975 20.372 2.445 180.063 .000b
ICICI .934a .872 .836 15.110 1.478 24.443 .000b
Findings from the regression analysis results for the selected banks and
macroeconomic variables as depicted in Table12. Indicates that, the R Square of SBI, BOB,
PNB, HDFC, KMB and ICICI is .430, .189, .296, .985, .980, and .875. The R Square which
is also measure of the overall fitness of the model indicated that the model is capable of
explaining about 43, 18, 29, 98, 98 and 87 percent of the variability the share price of banks.
This results is complimented by the adjusted R-Square of about 40%. -30%, 10%, 98%, 97%
and 83% which is essence is the proportion of total variance that is explained by the model.
The F statistics which is a proof of the validity of the estimated as reflected in
Table.12 indicated that, the hypothesized relationship between banking sector and
macroeconomic variables are validated. F is about 16.59, .83, 105.16, 180.06, .24.443 and a
p-value that is equal to 0.05 (p-value =.05), that invariably suggests clearly that
simultaneously the explanatory variables are significantly associated with the dependent
variable. Share price of banking sectors are strongly determined. Further Durbin-Watson
statistics value is 2.44 which means that the error term is independent and is free of
autocorrelation.
SUGGESTION
The strength of an investors is the should consider all relevant sources of information
when making an investment decision it is strengthen of investors.
When the investors are at risk they would not be happy to invest in a highly
fluctuating stock. Whenever those with a thirst ofriskiness would happily invest in a
highly volatile market.
From the study the selected banking sectors are different in their operations even the
share volatility is similar for all the selected banks.
The banks are suggested to improve the performance and prevailing market
condition to reduce market volatility of share price that are becoming high volatility
heads to high risk.
The Macroeconomic variables along with share price fluctuations indicatestrongly, to
the investor to attain clarity while investing and also create a stable economy by
increasing the flow of cash among the banking sectors.
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CONCLUSION
The study performed necessary analyses to answer the research questions of whether
some of the identified macroeconomic factors can influence the banking share price and
volatility of the share price decides the degree of risk and return one investor can earn form
stock market, so information regarding to the share price volatility helps the investors to
make the best investment decisions. The study is such an attempt, to examine the volatility
and effect of macroeconomic variables of selected banking sectors listed in NSE for
providing valuable information to investors to succeed.
REFERENCE
Indian Journal of Finance
International journal of economics and finance
International journal of business and management
International journal of Research and applied natural and social science.
WEBSITES
www.nse.com
www.yahoofinance.com
www.worlddatabank.com
Google Scholar
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