INTRODUCTION:
Oil prices affect every corner of economic activity around the world. The countries producing and consuming oil were significantly affected by fluctuations in world oil prices. India fulfills its advanced crude oil demand by importing it from oil-producing countries. Any fluctuations in crude oil also affect other industrial segments. The higher price of crude oil is associated with a higher price of energy, which, when rotated, adversely affects other trading practices that are directly or indirectly dependent on it.
Investors are responding to rapidly changing oil prices for their interests, as they are influenced by different industries. In the short term, the price of crude oil depends on many factors, such as social and political actions, the state of financial markets, but in the medium and long term, it is influenced by the basic demand and supply requirements, which results in the adjustment of its own price. In the context of India, OPEC ranks first among the ten largest oil-consuming countries.
The buoyancy in the global economy has strengthened the appetite for crude in recent months. During January-March, oil consumption worldwide increased by almost 2% year-on-year, which was mainly due to demand from the United States, China and other Asian countries. The decision of the United States to unilaterally withdraw from Iran’s nuclear bid and the sanctions that are expected to be imposed on Iran as a result are among the main factors behind the recent rally in oil prices. Iran currently exports about 2.5 million barrels of oil per day, which is about 4 percent of the total supply in the world, and recently introduced restrictions may reduce this figure by as much as 1 million barrels per day. On the macroeconomic front, higher oil prices mean rising inflation, disrupting fiscal policy, rising prices for gasoline and diesel fuel, and increasing the likelihood of higher interest rates. But according to trends, rising oil prices also mean a higher inflow of funds and a rally in the stock market.
For the study daily spot prices data and market returns data have been collected. Crude oil prices are termed as independent variable where as stock market return is treated as dependent variable for the selected study period.
REVIEW OF LITERATURE:
The recent volatility in the crude oil market has sparked interest in oil prices and its effect on macroeconomic factors, such as inflation, the exchange rate, employment levels, growth rates and stock returns. There is a wide range of literature devoted to the impact on the rise in oil prices and fluctuations in oil prices and its impact on the stock market. Huang, Yang, and Juan Li (2007) and Mork (1989) investigated the relationship between changes in oil prices that affect inflation and found a significant relationship between oil prices and inflation rates. Jones and Kaul (1996) studied the effects of oil market shocks on the stock market by analyzing the current and upcoming fluctuations in the cash flow of expected market returns. They studied developed and regulatory environmental systems and found that oil prices predict stock returns, with the exception of England. Miller and Ratti (Miller and Ratti, 2009) explored the long-term relationship between oil prices and stock markets using VECM. They find a long-term relationship between oil prices and stock returns. Dhaoui and Naceur (2014) found a strong negative association in some seven countries between oil prices and the stock market return. Sandorsky (1999) and Papapetru (2001) empirically showed that raw materials stocks have a negative and significant initial impact on stock returns. Inayat (2010) investigated the relationship between oil prices and the economic performance of European companies. He came to the conclusion that the oil does not have a significant negative impact on the auto report. However, high-end car manufacturers have shown instability in the return of shares during the analyzed period. Jungwook and Ronald (2008) investigated the impact of oil price shocks and stock markets in a developed market context and concluded that oil price shocks cause 6 percent volatility in stock returns. Ready (2013), came to the conclusion that oil demand shocks positively correlate with stock returns, but oil supplies in the districts have a negative correlation with stock returns. Maghyereh and Akttam (2004) contradicted the opinion of Jungwook and Ronald (2008) using VAR models and concluded that oil price shocks do not have a significant impact on the index yield. Ono (2011) examined the impact of crude oil prices on the stock markets of the BRIC countries. He concluded that the return of shares of China, Russia and India has a positive effect, and the profitability of stocks in Brazil has no statistical significance. Chen (2010), investigated the relationship between high oil prices and its effect on stock market returns, taking the S & P 500 price index as a proxy. The results of the study showed that there is a high probability of a bear market appearing as a result of rising oil prices. Nandha and Faff (2008) examined the short-term relationship between oil prices and 35 major global industries.
Although the bulk of the literature focuses on fluctuations in the different macroeconomic variables. In the current study, an attempt is made to analyze the relationship between oil volatility and its effect on Indian stock returns.
RESEARCH DESIGN AND METHODOLOGY:
The purpose of the study is to study the relationship between oil prices and their impact on the Indian stock market over the period from 2011 to 2018. The study considered the BSE Sensex as a dependent variable and crude oil prices (MCX CR) as an independent variable. Data for selected variables were taken on daily basis during the period from April 2011 to March 2018. For the purpose of the study, the required secondary data was collected from reports, working papers, newspapers and the BSE and MCX statistical database. Various statistical and econometric measures; descriptive analysis, unit root test (ADF), causality bordering on the VAR framework used for the analysis. To conclude a VAR model, it is necessary to select symmetric lags for all variables in the model using a statistical deciding factor, such as AIC or SIC.
Researcher employed Unit Root test to judge the null hypothesis (Ho) that the variable contains a unit root (non stationary). Augmented Dickey-Fuller (ADF) test is the most popular unit root test to test the stationarity. Augmented Dickey Fuller (ADF) test for unit root for checking the favor of stationary was developed by Dickey and Fuller (1979), ADF model is:
Where, k is the number of lag, ytis the time series data under consideration. The test is based on the null hypothesis (Ho) that the variable contains a unit root or non-stationary, and alternative hypothesis (H1) is that the variables are generated by a stationary process. This test requires a negative sign and significant test statistic, for rejecting the null hypothesis.
To fulfill the aim of present research, the study need to explore causal link and has applied Granger Causality Test (Granger, 1969) in the Vector Auto Regression framework. The VAR model is a multi-equation system where all the variables are treated as endogenous variable. There is one equation for each one variable as dependent variable. The use of VARs for causal inferences is known as structural modeling.
The mathematical presentation of Granger Causality test for autoregressive model is:
Where, p is the maximum length of the lagged
observations, A is the matrix that contains the coefficients of the
model (containing of each lagged values of variables), and 
and 
are the prediction errors.
ANALYSIS & FINDINGS:
To assess the impact of crude oil prices changes on daily stock market returns of the stock markets. Researcher established the relationship between these two variables by applying correlation analysis. Box (a) and Box (b) have the descriptive statistics for both variables; MCX and BSE Sensex.
Box (a): Descriptive Statistics for MCX Crude:
|
Series |
MCX Crude |
|
Mean |
.0489 |
|
St. deviation |
2.565 |
|
Kurtosis |
5.398 |
|
Skewness |
.527 |
|
Minimum |
-14.29 |
|
Maximum |
15.78 |
Box (b): Descriptive Statistics for BSE Sensex:
|
Series |
BSE Sensex |
|
Mean |
.0496 |
|
St. deviation |
2.061 |
|
Kurtosis |
8.601 |
|
Skewness |
.670 |
|
Minimum |
-10.96 |
|
Maximum |
17.34 |
The results of descriptive in Box (a) and Box (b) are show that the mean of % changes in BSE returns is .050 and the MCX Crude mean is .049. St. Deviation for both the series is 2.06 and 2.57 respectively. The kurtosis and skewness presents the view of normality. BSE has the relative normal leptokurtic distribution as the kurtosis is more than normal kurtosis; 8.61, and MCX has 5.39 which is relatively normal leptokurtic.
To test the hypothesis related to unit root testing, researcher has employed Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests of unit root. This requires a negative sign and significant test statistic, for rejecting the null hypothesis. Table 1 shows the results of this analysis. This testing of unit root hypothesis, reveals that the selected datasets (variables series); MCX Crude and BSE Sensex are stationary. And researcher has rejected the null hypothesis at different levels of significance; 1%, 5% and 10%.
