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Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-10, 2016 ISSN: 2454-1362, http://www.onlinejournal.in

Imperial Journal of Interdisciplinary Research (IJIR) 728

Impact of Crude Oil Price Volatility on Firm’s Financial Performance Empirical Evidence

from Indian Petroleum Refining Sector

Mr. Prasad V. Daddikar1 & Dr. Mahesh Rajgopal2 1Research Scholar, UoM & Assistant Professor, BET’s Global Business School

2Associate Professor, DOS in Business Administration, University of Mysore [UoM]

Abstract: The price volatility represents a significant source of risk to business organizations those belongs to resource intensive sectors. Therefore, impact of price volatility on the real economy is greatest in the present dynamic business environment. Hence, crude oil price volatility analysis has drawn considerable interest from academicians, investors, economist, financial analysts and risk management practitioners in recent years as it touches virtually every economic entity—from individuals, to organizations, to the economy. This study empirically analyses the impact of crude oil price volatility on firm value & financial performance of Indian petroleum refining sector firms. The data has been sourced from Multi Commodity Exchange, India, various AGM reports of sample firms and analyzed using econometrics techniques with the help of EViews. Based on AIC & SIC principles, the study revealed that GARCH (1,1) and EGARCH(1,1) models with student’s t distribution were able to capture the symmetric & asymmetric volatility estimates of crude oil prices. Univariate regression analysis shows partial impact of crude oil price volatility on firm value and financial performance indicators of sample firms. Effects of crude oil price volatility were not uniformly significant across the sample firms since their principal operations were directly or indirectly influenced by ownership pattern, operational diversification, economies of scale/scope, exposure to international trade and other firm specific qualitative/quantitative factors. KEYWORDS : Crude oil, volatility, stationary, heteroscedasticity, enterprise value, regression analysis etc. JEL CLASSIFICATION CODE: C22, C53, C58, G32 INTRODUCTION The past decade has witnessed extraordinary volatility patterns in the energy commodity prices which is typically greater than the variability observed in other financial risk factors such as currency exchange rates and interest rates. The

price volatility represents a significant source of risk to business organizations those belongs to resource intensive sectors. Hence, impact of price volatility on the real economy is greatest in the present dynamic business environment. Therefore, crude oil price volatility analysis has drawn considerable interest from academicians, investors, economist, financial analysts and risk management practitioners in recent years as it touches virtually every economic entity—from individuals, to organizations, to the economy. Individuals need to manage this risk to protect their real incomes, firms to protect their profitability and competitiveness, and the economy to protect its macroeconomic stability. Swift expansion in emerging Asian and Latin American markets, combined with the influence of hedge funds and market speculators recently boosted crude oil prices to historic levels. These same forces, when combined with the realities of a global economic recession and an adverse credit environment, had the opposite effect, causing the price of crude oil to plummet with alarming speed. This phenomenal volatility of crude oil prices not only unfavorably influences the top line and bottom line of individual firms, but it also places the markets and industries at high risk that are heavily dependent on crude oil for their principal operations. An investigation by a US Senate committee estimated that, “over the past few years, large financial institutions, hedge funds, pension funds, and other investment funds have been pouring billions of dollars into the energy commodities markets, perhaps as much as $60 billion in the regulated US oil futures market, to try to take advantage of price changes or to hedge against them”. As fund managers increased their stakes in energy commodities, institutional investors poured huge sums into the market to balance portfolios. As a result, market prices diverged from predicted levels when prices were based solely on the underlying fundamentals of strong demand for energy and industrial commodities. Business organizations that take a traditional approach to manage crude oil prices are able to address mild volatility but not large or sustained

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Imperial Journal of Interdisciplinary Research (IJIR) Page 1729

increases or decreases in prices. Traditional approaches in managing crude oil price fluctuations generally employ a series of risk management activities, including procurement contracts, financial hedging, passing on price increases to customers, accepting cost increases in order to mitigate the adverse effects of price volatility on firm’s financial performance. These approaches result in risk management programs that are often reactive in nature and biased toward market opportunities and short-term tactics, which leads to excessive trading costs and the potential for trading losses. Indeed, in the past decade as crude oil prices have demonstrated cyclical variability, many firms have initiated or expanded hedging programs to stabilize their financial performance. This empirical research paper comprises of following sections. Section 1 provides a brief review of relevant literature. Section 2 specifies objectives of the paper. Section 3 discusses the overview of Indian petroleum refining sector. Section 4 describes the concepts of crude oil price volatility. Section 5 represents the methodology and data description. Section 6 contains the empirical analysis, section 7 offer findings and section 8 concludes the paper. 1 LITERATURE REVIEW Huang R., Masulis R. and Stoll H. (1996), reveals that at the micro level, changes in the price of oil, a key factor in the production process, affects financial performance, cash flows of firms, in turn influencing firms’ dividend payments, retained earnings and equity prices. Perry Sadorsky (1999), using a vector auto regression showed that oil prices and oil price volatility both play important roles in affecting real stock returns. There is also evidence that oil price volatility shocks have asymmetric effects on the economy. Haushalter G. D., Heron R. A., Lie E. (2002), in their study examines the sensitivity of equity values of oil producers to changes in the uncertainty of future oil prices. It was concluded that corporate risk management can increase shareholder value by reducing the expected costs of financial distress and underinvestment. Hammoudeh S., Dibooglu S. and Aleisa E. (2004), employed univariate and multivariate GARCH to examine volatility persistence in the crude oil market and its effect on the equity return volatility of the S&P oil sector indices. Robert S. Pindyck (2004), in this paper examines the behavior of natural gas and crude oil price volatility in the United States since 1990 and says there exists some evidence that crude oil volatility and returns have predictive power for natural gas volatility and returns, but not the other way around.

Jin Y. and Jorion P. (2006), in their paper studied the hedging activities of 119 U.S. oil and gas producers from 1998 to 2001 and evaluates their effect on firm value. They verified that hedging reduces the firm's stock price sensitivity to oil and gas prices, however, they found that hedging does not seem to affect market value for this industry. Boyer M. M. and Filion D. (2007), find that the return of Canadian energy stock is positively associated with the Canadian stock market return, with appreciations of crude oil and natural gas prices, with growth in internal cash flows and proven reserves, and negatively with interest rates. Buhl H. U., Strauß S. and Wiesent J. (2011), analyzed the dependency of hedge financing and derived optimal hedging extents for companies in different market situations based on a long-term model. By hedging the commodity price, companies can realize a surplus in profits. Dayanandan A. and Donker H. (2011), investigated the relationship between commodity prices of crude oil, capital structure, firm size and accounting measures of firm performance. Findings show that crude oil prices positively and significantly impact the performance of oil and gas firms in North America using accounting measures of performance. Narayan P. K. and Sharma S. S. (2011), examined the relationship between oil price and firm returns for 560 US firms listed on the NYSE, oil price affects returns of firms differently depending on their sectoral location, lagged effect of oil price on firm returns and unravel that oil price affects firm returns differently based on firm size, implying strong evidence of size effects. Ramos S. B. and Veiga H. (2011), found oil and gas sector in developed countries responds more strongly to oil price changes than in emerging markets. Oil and gas industry returns also respond asymmetrically to changes in oil prices; oil price rises have a greater impact than oil price drops. Robert C. Ready (2013), developed an innovative technique for classifying oil price changes as supply or demand driven and documented several novel facts about the relation between oil prices and stock returns. Demand shocks are strongly positively correlated with market returns, while supply shocks have a strong negative correlation. Peter Christoffersen and Xuhui (Nick) Pan (2014), said in the post-financialization period, oil volatility risk is strongly related with various measures of funding liquidity constraints suggesting an economic channel for the effect. Farhad Taghizadeh-Hesary, Ehsan Rasolinezhad, and Yoshikazu Kobayashi (2015) tried to shed light on the impact of crude oil price volatility on each sector in Japan, the world’s third-largest crude oil consumer.





Indrani Hazarika (2015), in her paper analyzed the financial performance of top five oil and gas companies worldwide and revealed that fluctuating oil prices do not significantly impact the profitability, liquidity, efficiency and financial health of top oil and gas companies. Zaabouti, K., Ben Mohamed, E. and Bouri, A. (2016), assessed the potential impact of oil price changes on the value of firms using a stochastic frontier approach, an attempt was made to derive the optimal value Q* of firms and calculate the Q value observed. The fact that variation of oil prices can largely explain distortions in the value of firms was empirically demonstrated. 2 OBJECTIVES • To offer an overview of Indian petroleum

refining sector. • To study the significant concepts of crude oil

price volatility. • To measure the volatility of crude oil price

returns using suitable GARCH family model. • To analyze the impact of crude oil price

volatility on firm value and financial performance indicators with reference to firms of Indian petroleum refining sector.

3 OVERVIEW OF INDIAN PETROLEUM REFINING SECTOR The Indian Petroleum refining sector has come a long way since crude oil was discovered and the first Refinery was set up at Digboi in our country more than a century and decade ago. The present Mumbai Refinery of HPCL was the first modern Refinery to be set up after independence by Esso in 1954, which was followed by setting up of Refineries by other oil majors. Since then, refineries were established by the Government, Private sector and Joint sector. Indian refining industry has done well in establishing itself as a major player globally and today we are the 4th largest country in the world in terms of refining capacity which is presently 230.066 Million Metric Tonnes Per Annum (MMTPA) after USA, Russian Federation and China. There are 23 refineries in India, out of which 18 are in public sector, 3 in private sector and 2 are joint ventures. The capacity, configuration and complexity of the refineries have undergone major changes with phase-wise capacity expansion and modernization. Starting with a simple hydro skimming refinery configuration in 1950s, comprising simple crude oil distillation, naphtha/kerosene/ATF treatment and catalytic reforming for upgrading naphtha to petrol, the PSU refineries started adopting state of art modern technologies. The first oil shock of 1973 heralded a major change in the refining industry and the process configuration and technology.

Configuration of Indian refineries further underwent a major change in late 1980's and early 1990's. The first hydrocracker in the country was commissioned in Gujarat refinery of IOCL in 1993. All new grass root refineries started considering installation of hydrocrackers, cokers. Refinery configurations in late 1990's were dictated by the product quality up-gradation due to environmental considerations. The refining capacity is not only sufficient for domestic consumption but leaving a substantial surplus also for export of petroleum products. Since 2001-02, India is a net exporter of petroleum products. During 2015-16, the country has exported 43.779 Million Metric Tonnes (MMT) of Petroleum products worth US Dollars 21.438 Billion. India is the largest exporter of petroleum products in Asia since August 2009. 3.1 Refinery Performance Improvement Indian public refineries are equipped with modern technologies and continuously upgrade the technologies in line with the International trend and as per the requirement. Indian refineries have accorded top priority to reduce the energy consumption through various energy conservation measures. Adoption of modern technologies by Indian refineries and energy conservation measures has helped in increasing the distillate yield, quality upgradation of petrol/diesel and reduction in Specific energy consumption (MBTU/Bbl/NRGF-MBN). The industry average distillate yield has improved from 73.3% in 2005-06 to 78.5% in 2014-15. Similarly the industry average MBN has come down from 76.4% in 2005-06 to 62.0% in 2014-15 which is a good indicator of performance improvement. 3.2 Domestic crude oil production and refining To meet ever-growing demand for petroleum products, the government has consistently endeavored to enhance exploration and exploitation of petroleum resources, along with developing a concrete and structured refining, distribution and marketing system. Due to this, crude oil production and refining increased at around 39.7 mmt in FY15 as against 37.8 mmt in FY14, showing an increase of about 5.0%. 3.3 Private refiners earns higher GRMs than the public players Gross refining Margins (GRMs), a key industry measure of profitability, at several refineries of Indian state-run firms are lower than those of private companies. The lower GRMs of government run companies are largely on account of comparatively higher operating costs they incur. Moreover, it is widely believed that while the private players calculate their GRMs on actual





price, the public sector players like IOCL base their GRMs on price at which the crude has been sourced. 3.4 Declining crude oil prices In FY15, India’s average crude oil import price (Indian basket) plunged to US$60.3/bbl from US$105.52/bbl, while, the international Brent crude prices have dropped 40% since June’14 to US$74 a barrel recently, giving some relief to India and China, the major crude oil importers. The fall in prices is largely due to subdued demand by consumers and oversupply by some OPEC producers. An added reason is the increase in the US production from shale. India imports more than two-thirds of its requirement, which constitutes 37% of total imports. A one-dollar fall in the price of oil saves the country 40 billion. The benefits of declining crude oil prices on Indian refining firms are already visible. On earnings front, the profitability of downstream PSUs is likely get a significant boost in FY16 because their interest costs will decrease with the decline in their working capital requirements, and they would not have to bear any under-recovery burden. 3.5 Higher investments provide long term positive outlook The Public Sector Oil Companies, IOCL, BPCL, HPCL and EIL are planning to invest approximately Rs. 1.5 lakh Crore in setting up India's biggest refinery on the West Coast. The proposed refinery would have a capacity of 60 MMTPA which will be built in 2 phases, 40 + 20 MMTPA. It would be accompanied by a petrochemical complex. 3.6 Future Outlook The outlook for Indian refining sector looks stable for both public and private sector companies. For the downstream segment, the long-term prospects continues to be bright, based on the sizeable potential for petrochemicals and demand growth. As per International Energy Agency (IEA), over the medium-term (2020) India will continue to be a net exporter of refined petroleum products and over the long-term (2035) it will become the largest source of global oil consumption and a net importer. Post diesel deregulation, we expect fall in under-recoveries, which in turn would lead to lower borrowing levels and interest burden, thereby resulting in improvement in profitability and liquidity position of the OMCs. Following an outlook of subdued international refining margins and moderate import-duty differentials between petroleum products and crude oil, GRMs of domestic refineries are also expected to remain weak over the medium term. Their profit metrics will continue to be sensitive to the volatility in

Indian Rupee (INR) against the US Dollar (USD) parity levels, inventory gains/losses arising from volatility in crude prices and import duty protection levels. 4 CONCEPTS OF CRUDE OIL PRICE VOLATILITY The term volatility has been given different definitions by different scholars across disciplines. Price volatility refers to the degree to which prices rise or fall over a period of time. In an efficient market, prices reflect recognized existing and anticipated future circumstances of supply and demand and factors that could affect them. Changes in market prices tend to reflect changes in what markets collectively anticipate. When market prices tend to change a lot over relatively a short time, the market is said to have high volatility. When relatively stable prices prevail, the market is assumed to have low volatility. In relation to crude oil price, volatility is the variation in the worth of a variable, especially price (Routledge, 2002) as cited in (Busayo, 2013). Volatility is the measure of the tendency of oil price to rise or fall sharply within a period of time, such as a day, a month or a year (Ogiri et al. 2013). Lee (1998) as cited in Oriakhi and Osazee (2013) defines volatility as the standard deviation in a given period. In a nutshell, volatility is a measurement of the fluctuations (i.e. rise and fall) of the price of commodity for example crude oil price over a period of time. It has been witnessed that presently the price of crude oil does not seem to be amplified by traditional demand and supply relationships, but by dynamics of interlinked financial markets and changing Geopolitical landscape. Several factors have been identified as causes of oil price volatility; these factors range from demand and supply of crude oil, OPEC decisions, economic downturn, oil derivative contracts, exchange rates, gold prices etc. The political and in some cases military upheavals in Nigeria, Venezuela, Libya, Egypt, Syria, and other MENA countries, the boycott of Iranian crude oil in response to its nuclear weapons program, and the risk of terrorist attacks all have conspired to make oil markets more volatile. Thus so far these events have not seriously disrupted oil supplies, but it is conceivable that they could. Merino and Ortiz (2005) adopt the traditional approach to assessing the tightness of the oil market, they states that the evolution of oil inventories should reflect the interaction between supply and demand forces, which should contribute in explaining oil price changes. The unexpected economic developments could shake crude oil markets and increases volatility. The fear of global shortage of crude oil may also account for changes in oil price. As noted by Appenzeller (2004), there have been diverse





arguments about how much more of crude oil reserve the world has before the wells dry up. Although, history has it that oil price shocks were mainly caused by physical disruptions of supply, the price run-up of 2007- 2008 was caused by strong demand confronting world production (Hamilton, 2009; Cale, 2004). Oil price fluctuations heavily affect consumers, producers and the overall incentive to invest. 5 METHODOLOGY This paper has employed causal research design to analyze the impact of crude oil price volatility on firm’s financial performance taking into consideration top five firms from Indian petroleum refining sector that are publicly traded on national stock exchanges, i.e. BSE/NSE. The daily crude oil price were averaged to yearly prices and used as an independent factor to assess the effects of price

volatility on the firm’s financial performance in terms of profitability, liquidity, efficiency and firm value as dependent factors by using appropriate econometrics techniques/models. 5.1 Sample Design From the total population the sample selected using stratified by sector/industry in order to better understand the characteristics of the homogeneous subsets (Albright, 2006). The next part of the selection required that all companies in the sample were listed on the national exchanges for the time span, 2006 to 2015. The study tried to conduct longitudinal time series analysis in order to identify relationship and effects between select independent and dependent factors. The sample size has been restricted to top five companies of Indian petroleum refining sector based on total income, net profit and are reported in table no. 1.

Table No. 1 Key Financial Particulars of Select Sample Firms Firm Name Total Income Net Profit

Reliance Industries Ltd. (RIL) 337797.00 22719.00 Indian Oil Corporation Ltd. (IOCL) 441670.18 5273.03 Bharat Petroleum Corporation Ltd. (BPCL) 240286.86 5084.51 Hindustan Petroleum Corporation Ltd. (HPCL) 207794.60 2733.26 Mangalore Refining Petrochemicals Ltd. (MRPL) 58267.52 -1712.23

Source: Compiled, edited data from AGM reports, 31st March 2015 [Fig in Rs. Crores] 5.2 Data Description The data consists of daily closing prices of Crude Oil which is traded on Multi Commodity Exchange (MCX), India. The study has used spot prices of crude oil with a total of 2960 usable observations based upon a time interval ranging from 2006 to 2015. The financial performance analysis of the sample firms have been carried out using ratio analysis and firm value proxy, i.e. enterprise value. Conducting comparisons using financial ratios avoids the problem of comparing companies of different sizes (Firer et all, 2004). 5.3 Techniques of Data Analysis The paper has utilized time series data which was initially studied using basic descriptive statistical tools and further analysis was carried out using advanced econometrics techniques. Time series data have been tested for stationarity and unit root using ADF and PP tests. Crude oil price volatility modelling was done using appropriate GARCH family models. Univariate regression model was employed to establish the relationship between the dependent and independent variable and causal impact was ascertained referring to result of regression analysis in the following section.

6 EMPIRICAL ANALYSIS The empirical data analysis was performed in three broad phases along with necessary sub-phases for better interpretation and inference depiction. The first phase deals with crude oil price volatility analysis, second phase comprises of descriptive statistical, normality and stationarity study of select financial performance indicators related to sample firms and the third phase consists of univariate regression analysis to ascertain the impact of crude oil price volatility on select financial performance indicators of sample firms in the following section. 6.1 Crude oil price volatility analysis Table No. 2 Descriptive Statistics & Normality

Test Particulars Crude oil price returns

Mean -0.002901 Std. Dev. 2.118504 Maximum 17.48352 Minimum -14.19558 Skewness 0.200628 Kurtosis 9.180412 Jarque-Bera 4730.881 Probability 0.000000

Source: Compiled, edited data from MCX & computed using EViews 7

The descriptive statistical analysis was carried on the daily returns series spot price of crude oil. The





mean value of return series is negative, demonstrating the fact that crude oil prices have decreased during the reference period of this study. It was evident that the crude oil prices are reducing post global economic crisis, the recent commodity super-cycle bust and the geo-political instability in the MENA region. The maximum and minimum values have demonstrated wider spread for the spot price return series and this result supports the presence of volatility patterns is the return series. The recorded skewness value is positive, signifying the tail on the right side is longer or fatter than the left side and thus it validates the nonconformity of normal distribution. The kurtosis had revealed an excessive positive value, representing leptokurtic nature of heavy-fatter tailed distribution and it denotes non-normality of data. The Jarque-Bera test is significant at 1% level and therefore justifies rejection of null hypothesis, i.e. ‘return series is normally distributed’ and accept the alternative

hypothesis, i.e. ‘returns series is not normally distributed’. The descriptive statistical outcome is similar to past studies performed on crude oil price volatility and facilitates to apply GARCH models in order to assess the volatility patterns on the documented time-series data. 6.1.1 Stationarity Test for Crude Oil Price It was witnessed that crude oil prices have changed over time due to influence of long memory, internal, external factors and demonstrated volatility clustering for financial series. This indicates that low volatility patterns have a tendency to follow small volatility patterns for an extended time period and the high volatility tend to be followed by large volatility for a prolonged time period. Therefore, it justifies the volatility is clustering and the price vary around the constant mean but the variance is changing with time.

Table No 3 Unit root test at levels Particular ADF test PP test

t-statistics -59.10764 -59.08978 Prob.* (p-value) 0.000 0.000 *Mackinnon (1996) one-sided p-values.

Source: Compiled, edited data from MCX & computed using EViews 7 Since the data has been non-normal, it is necessary to check the stationarity of returns series using ADF and PP tests. The observed outcome of both the tests have shown significant result as p-values are < 0.01 and rejection of null hypothesis, i.e.

‘crude oil price returns are non-stationary and times series data have a unit root’ has been justified. Therefore it is concluded that the returns series are stationary at levels, representing the mean reverting feature.

6.1.2 Pre-Assessment Investigation for Volatility Analysis

Table No. 4 ARCH-LM Heteroskedasticity Test for Crude oil price series F-statistic 172040.1 Prob. F(1,2957) 0.0000

Obs*R-squared 2909.001 Prob. Chi-Square(1) 0.0000 Source: Compiled, edited data from MCX & computed using EViews 7

The pre-assessment investigation was carried out in three steps as suggested by Engle (1982) and other research scholars/authors. The first step uses the descriptive statistical analysis, second step is in the form of a graphical analysis and the third step utilizes ARCH LM test. Table no. 2 provides details pertaining to crude oil price returns series using various descriptive statistical techniques and normality test. As a final step, the ARCH-LM test was employed in order to quantitatively justify the existence of autoregressive conditional heteroscedasticity in the returns as presented in the table no. 4 and it is concluded that the ARCH-LM test is highly significant, since the p-value is less than one percent significance level (0.000 < 0.01). Therefore, the null hypothesis, i.e. ‘there is no ARCH effect’ is rejected and the alternative hypothesis, i.e. ‘there is an ARCH effect’ is accepted at 1% level of significance, which confirms the existence of autoregressive conditional heteroscedasticity effects in the residuals of crude oil price returns series. Thus, the

pre-assessment investigation results permit for the volatility analysis using appropriate GARCH family models. 6.1.3 Volatility modeling using GARCH This section of the paper deals with the volatility analysis of crude oil price returns series using symmetric and asymmetric GARCH family models along with diagnostic test results in order to determine whether there exist any remaining autoregressive conditional heteroscedasticity effect in the residuals of the assessed GARCH family models. Since the normality and heteroscedasticity tests were highly significant as it was learnt in the above sections, hence it is concluded that residuals are not conditionally normally distributed. In such circumstances, selection of error distribution option requires a special consideration for computation of parameter estimates related to select symmetric and asymmetric volatility GARCH family models. The paper has used Student’s t error distribution with Berndt-Hall-Hall-Hausman (BHHH) optimization





algorithm for iterative process based on the past literature references to arrive at the best model fit for crude oil daily price returns volatility analysis. The leptokurtic heavy-fatter tailed crude oil price returns series distribution supports the selection of Student’s t option.

Based on pilot test results it has been realized that GARCH (1,1) and EGARCH(1,1) models with student’s t distribution were better able to capture the symmetric and asymmetric volatility estimates of crude oil price returns series respectively.

Table No. 5 Results of volatility modeling Error distribution Student’s t Volatility Model GARCH(1,1) EGARCH(1,1)

Coefficients of Mean Equation μ (Constant) 0.034438

(0.2210) 0.025269 (0.3658)

Coefficients of Variance Equation ω (Constant) 0.025979

(0.0093) -0.055637 (0.0000)

α (ARCH effect) 0.047598 (0.0000)

0.087149 (0.0000)

β (GARCH effect) 0.948046 (0.0000)

0.994195 (0.0000)

γ (Leverage effect) -- -0.039291 (0.0000)

AIC 4.001219 3.995461 SIC 4.011342 4.007608 ARCH-LM Test Result Test statistics 0.274015 3.361246 Prob. Chi-Square (1) 0.6007 0.0667

Correlogram Squared Residuals Test Result (36 Lags) Q-Stat Insignificant Insignificant Prob. Insignificant Insignificant

Source: Compiled, edited data from MCX & computed using EViews 7 The mean equation has been expressed as a function of exogenous variable with an error term and the constant term (μ) was found to be insignificant in both the models at all standard levels of significance. The parameter estimates of the GARCH (1,1) in variance equation was found to be significant at 1% level. ARCH effect coefficients (0.047598) is highly significant with positive value and it illustrates that information related to past volatility has an influence of current volatility. GARCH effect coefficients (0.948046) is also significantly positive, which implies that previous period’s forecast variance has an impact on current volatility. Since the GARCH effect is much larger than ARCH effect it look likes the market has a memory longer than one period and volatility is more sensitive to its lagged values than it is to new surprises in the market. The significant ARCH and GARCH values also suggest the influence of internal dynamics on the crude oil price volatility. The sum of α and β is 0.995644 and this is approximately equal to one. This specifies the shocks to volatility are highly persistent and the impacts of these shocks would endure in future periods too for a longer duration. Thus, the analysis shows that memory of shocks or surprises are

recollected in relation to daily spot price of crude oil price returns volatility. The diagnostic tests were conducted in order to verify the correct model specification. The ARCH-LM test was used to analyze the remainder of additional ARCH effect if any and the test statistic reported insignificant outcome at all standard levels of significance. Since the p-value > 0.05, the null hypothesis, i.e. ‘there is no ARCH effect’ is accepted. Further, the correlogram squared residuals test was found insignificant at all standard levels and it supports the acceptance of null hypothesis, i.e. ‘there is no serial correlation in the residual’. Therefore, it is conclude that model specification was accurate on the basis of insignificant diagnostic test results. The parameter estimate coefficients of EGARCH (1,1) in variance equation was observed to be highly significant at 1% level. This model is utilized to scrutinize the presence of leverage effect in return series of daily crude oil spot prices. The sum of α and β is more than one, which states greater persistent volatility having longer extension. Leverage effect (γ) is negative and significant at 1% level suggesting existence of leverage effect in return series and reporting diverse impact of previous periods good and bad news on the volatility. Thus, it is inferred that, past





period’s bad news effect is much greater than the influence of good news of the same quantum. The diagnostic tests were conducted to validate the model fit requirements and on the basis of insignificant diagnostic test results it is confirmed that model specification was correct. Based on the observed results it has been realized that crude oil prices in India were subjected to significant volatility in the past decade. In order to arrive at the best model of GARCH family, the AIC and SIC standards are used. The AIC and SIC principle stipulates, best model must have lower AIC and SIC value with respect to error distribution and optimization algorithm for iterative process. From table no. 5, it is discovered that

EGARCH (1,1) is the best model to estimate the crude oil price volatility for the sample data used in this paper. 6.2 Analysis of select financial performance indicators This section of the article provides a quantitative overview of select financial performance indicators in terms of firm value, profitability and efficiency ratios for sample firms. The enterprise value is taken as proxy for firm value analysis. Profitability was represented in the form of operating profit margin and net profit margin. Efficiency of sample firms has been expressed in terms of asset turnover ratio and inventory turnover ratio.

Table No. 6 Descriptive Statistics & Normality Test of Enterprise Value

Particulars RIL IOCL BPCL HPCL MRPL Mean 299202.9 115506.2 39973.18 32127.34 10968.67 Std. Dev. 84056.85 27164.87 14953.36 9016.178 3250.333 Maximum 400482.4 146156 68982.94 42396.17 15422.17 Minimum 130650.6 72830.40 20663.00 17565.61 6515.160 Skewness -0.685216 -0.27032 0.413151 -0.51494 0.153077 Kurtosis 2.548869 1.51946 2.547237 1.920708 1.624186 Jarque-Bera 0.897335 1.035118 0.369904 0.927306 0.827747 Probability 0.648128 0.595974 0.831144 0.628982 0.661084

Source: Compiled, edited data from AGM reports & computed using EViews 7

Table no. 6 summarizes descriptive statistical and normality test results of enterprise value related to sample firms. Enterprise value considers the entire economic value of a firm using relevant balance sheet items and reveals a true value of the firm. It was recorded that RIL had highest mean and maximum firm value among sample firms. This indicates professionally managed firm was able to create more value to its stakeholders as compared to the public sector enterprise owned and managed by government. The kurtosis revealed a positive value, representing platykurtic nature of shorter-thinner with a lower and broader peak. The Jarque-Bera normality test is insignificant at all standard levels and therefore justifies acceptance of null hypothesis, i.e. ‘data series is normally distributed’.

Table No. 7 Descriptive Statistics & Normality Test for operating profit margin Particulars RIL IOCL BPCL HPCL MRPL

Mean 11.45 3.67 2.50 2.027 3.00 Std. Dev. 2.74 1.106 0.776 0.627 3.02 Maximum 14.48 5.52 3.48 2.81 6.5 Minimum 7.95 2.23 0.84 0.62 -3.1 Skewness -0.066 0.11 -0.84 -0.97 -0.80 Kurtosis 1.35 1.78 3.20 3.60 2.60 Jarque-Bera 1.13 0.633 1.221 1.722 1.1459 Probability 0.56 0.728 0.5429 0.4227 0.5638

Source: Compiled, edited data from AGM reports & computed using EViews 7 Table no. 7 demonstrates that all sample firms are making adequate money from their operations to support the business activities and considered stable from its ongoing operations to pay for its variable costs as well as its fixed costs. From descriptive statistics, RIL topped among other

sample firms with highest mean and maximum operating profit margin but standard deviation shows greater variability as compared to other sample firms with an exception of MRPL which is more inconsistent.





Table No. 8 Descriptive Statistics & Normality Test for net profit margin Particulars RIL IOCL BPCL HPCL MRPL

Mean 8.829 2.087 1.189 0.92 1.52 Std. Dev. 2.926 1.077 0.57 0.44 2.16 Maximum 14.57 3.79 2.13 1.75 3.9 Minimum 5.63 0.96 0.38 0.43 -2.97 Skewness 0.598 0.345 0.12 0.42 -0.94 Kurtosis 2.361 1.58 1.93 2.054 2.90 Jarque-Bera 0.766 1.038 0.4971 0.6753 1.47 Probability 0.681 0.5951 0.7798 0.7134 0.4773

Source: Compiled, edited data from AGM reports & computed using EViews 7 Table no. 8 offers evidences to sample firm's production efficiency, cost structure and pricing policies. RIL was better able to strategize product mix for higher net profit margin among sample firms. MRPL has shown negative skewness value

in which left tail is long relative to the right tail and other sample firms have indicated a positive skewness, signifying the tail on the right side is longer or fatter than the left side in the distribution.

Table No. 9 Descriptive Statistics & Normality Test for asset turnover ratio

Particulars RIL IOCL BPCL HPCL MRPL Mean 90.586 199.01 295.85 262.94 237.16 Std. Dev. 16.956 15.75 46.96 31.13 53.155 Maximum 113.11 235.39 359.06 305.88 313.34 Minimum 57.71 184.28 221.32 207.65 167.35 Skewness -0.34 1.17 0.0955 -0.52 0.249 Kurtosis 2.58 3.45 1.799 2.24 1.67 Jarque-Bera 0.2655 2.377 0.6156 0.6873 0.84 Probability 0.8756 0.304 0.7350 0.7091 0.657

Source: Compiled, edited data from AGM reports & computed using EViews 7 Table no. 9 provide stakeholders an idea of how the sample firms are managing and utilizing its assets to produce products and sales over time. Referring to descriptive statistical results, it was witnessed

that all state-run firms have displayed better asset utilization capability and BPCL has been able to generate higher sales using all of its assets.

Table No. 10 Descriptive Statistics & Normality Test for inventory turnover ratio

Particulars RIL IOCL BPCL HPCL MRPL Mean 8.729 8.16 12.71 10.92 11.56 Std. Dev. 0.757 1.66 3.47 2.68 4.13 Maximum 9.57 12.21 19.65 15.93 20.25 Minimum 7.12 6.66 8.35 8.04 6.88 Skewness -0.947 1.56 0.69 0.64 1.068 Kurtosis 2.939 4.45 2.57 2.13 3.017 Jarque-Bera 1.498 4.93 0.8757 0.9969 1.901 Probability 0.4727 0.084 0.6454 0.6074 0.3864

Source: Compiled, edited data from AGM reports & computed using EViews 7 Table 10 shows effective inventory management by sample firms comparing cost of goods sold with average inventory during the reference period of the study. This measure shows that sample firms were able to turn its inventory with a minimum of nine cycles and maximum of twenty times over the years. The descriptive statistical results shows that public sector firms were more efficient in inventory turnover and BPCL was in a better position to control its merchandise for a higher inventory turnover. Finally this analysis provide empirical overview to creditors related to sample firm’s

efficiency and liquidity position since inventory is regularly put up as collateral for debt financing from banks and financial institutions. 6.2.1 Unit Root Test for Firm Value and Financial Performance Indicators In order to evaluate the stationarity of all select financial performance indicator series, unit root tests were executed at levels and differences. The tests used are the augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test. The results are summarized as follows.





Table No. 11 Unit Root Test for Enterprise Value Test Particulars RIL IOCL BPCL HPCL MRPL

ADF Test

t-statistics -2.9294 -3.8054 -3.7222 -3.5071 -3.7508 Prob.* (p-value) 0.0798 0.0266 0.0342 0.0441 0.0251

Stationary Levels 1st Diff 2nd Diff 2nd Diff Levels

PP Test t-statistics -5.7261 -3.8054 -3.9712 -3.3152 -4.6395

Prob.* (p-value) 0.0020 0.0266 0.0255 0.0462 0.0075 Stationary Levels 1st Diff 2nd Diff Levels Levels

Source: Compiled, edited data from AGM reports & computed using EViews 7 Table no. 11 displays the results of unit root test using the ADF and PP tests in which RIL and MRPL have indicated stationarity at levels, specifying limited exposure to seasonality and trend effects. For IOCL, the given data at level was

exposed to trend and seasonality effects, hence 1st order differencing was done to stabilize the mean of given time series. BPCL and HPCL have demonstrated stationarity at 2nd difference, representing mean reverting feature.

Table No. 12 Unit Root Test for Operating Profit Margin Test Particulars RIL IOCL BPCL HPCL MRPL

ADF Test


Stationary 2nd Diff 1st Diff Levels Levels 2nd Diff

PP Test t-statistics -3.3080 -12.6812 -4.5441 -4.7299 -8.6136

Prob.* (p-value) 0.0309 0.0000 0.0085 0.0067 0.003 Stationary 2nd Diff 1st Diff Levels Levels 2nd Diff

Source: Compiled, edited data from AGM reports & computed using EViews 7 Table no. 12 illustrates the outcome of unit root test using the ADF and PP tests in which BPCL and HPCL showed stationarity at levels, specifying partial exposure to seasonality and trend effects. IOCL was exposed to trend and seasonality effects

at levels, thus 1st order differencing was done to stabilize the mean of given time series. RIL and MRPL had revealed stationarity at 2nd difference, signifying mean reverting feature.

Table No. 13 Unit Root Test for Net Profit Margin

Test Particulars RIL IOCL BPCL HPCL MRPL

ADF Test t-statistics -4.5819 -3.9571 -10.2265 -4.2167 -3.3229

Prob.* (p-value) 0.0167 0.0259 0.0003 0.0158 0.0495 Stationary 2nd Diff 1st Diff 2nd Diff Levels 1st Diff

PP Test


Stationary 2nd Diff 1st Diff 1st Diff 1st Diff 1st Diff Source: Compiled, edited data from AGM reports & computed using EViews 7

Table no. 13 outlines the results of unit root tests in which HPCL has indicated stationarity at levels using ADF test, revealed restricted exposure to seasonality and trend effects. IOCL, BPCL & MRPL were exposed to trend & seasonality effects,

hence 1st order differencing was done to stabilize the mean of given time series. RIL demonstrated stationarity at 2nd difference, expressing mean reverting feature.

Table No. 14 Unit Root Test for Asset Turnover Ratio



Prob.* (p-value) 0.0645 0.0154 0.0137 0.0070 0.0032 Stationary No 1st Diff 2nd Diff 2nd Diff 1st Diff

PP Test


Stationary No 1st Diff 2nd Diff 2nd Diff 1st Diff Source: Compiled, edited data from AGM reports & computed using EViews 7





Table no. 14 represents the results of unit root tests using ADF and PP wherein RIL was not stationarity even after 2nd order differencing which is strange outcome. IOCL and MRPL were exposed to trend and seasonality effects at levels, therefore

1st order differencing was done to stabilize the mean of given time series. BPCL and HPCL have indicated stationarity at 2nd difference, representing mean reverting characteristic.

Table No. 15 Unit Root Test for Inventory Turnover Ratio



Prob.* (p-value) 0.0335 0.0203 0.0143 0.0124 0.0203 Stationary 1st Diff 1st Diff 1st Diff 1st Diff 1st Diff

PP Test


Stationary Levels 1st Diff Levels 1st Diff 1st Diff Source: Compiled, edited data from AGM reports & computed using EViews 7

Table no. 15 displays the results of unit root tests in which RIL and BPCL have indicated stationarity at levels using PP test, specified limited exposure to seasonality and trend effects. IOCL, HPCL and MRPL have exposed to trend & seasonality effects at levels, hence 1st order differencing was done to stabilize the mean of given time series. 6.2.2 Regression Analysis The following univariate regression model was used to investigate the impact of crude oil price

volatility on firm’s financial performance of Indian petroleum refining sector.

Y = β0 + β1X1 + µ Where, Y is dependent variable (financial performance indicators: EV, OPM, NPM, ATR, ITR) X1 is independent variable (crude oil price) β0 expresses intercept of the model β1 is coefficient of independent variable

µ represents the error term 6.2.2.1 Impact of Crude Oil Price on Enterprise Value

Table No. 16 regression results between crude oil price & Enterprise Value Test Particular RIL IOCL BPCL HPCL MRPL

Univariate Regression Test

R2 0.0619 0.5003 0.1507 0.4844 0.1096 F-statistic 0.5280 8.0116 1.4205 7.5185 0.9847

Prob. (F-stat) 0.4881 0.0221** 0.2674 0.0253** 0.3500 t-Stat 0.7266 2.8304 1.1918 2.7419 0.9923 Prob. 0.4881 0.0221** 0.2675 0.0254** 0.3501

Breusch-Godfrey Serial Correlation LM Test

F-stat 1.1357 1.6743 1.6766 0.5613 0.8853 Prob. F 0.3817 0.2644 0.2640 0.5977 0.4603 Obs*R2 2.7461 3.5819 3.5851 1.5762 2.2787

Prob. Chi-Sq. 0.2533 0.1668 0.1665 0.4547 0.3200

Heteroscedasticity Breusch-Pagan-Godfrey

Test

F-stat 1.0758 1.1738 1.5725 4.5197 3.8984 Prob. F 0.3300 0.3102 0.2452 0.0662 0.0838 Obs*R2 1.1854 1.2795 1.6427 3.6101 3.2764

Prob. Chi-Sq. 0.2763 0.2580 0.1999 0.0574 0.0703

Residuals Normality Test Jarque-Bera 0.6327 0.3219 5.0760 0.2010 0.4129 Prob. 0.7287 0.8513 0.0790 0.9043 0.8134

*, ** & *** indicate the 10%, 5% & 1% significance level of probability, respectively Source: Compiled, edited data from AGM reports & computed using EViews 7

The regression analysis in table no. 16 illustrates that impact of crude oil price volatility on IOCL and HPCL enterprise value is significant. Approximately 50% changes in the enterprise value of these firms was brought by the crude oil price volatility since R2, F-stat and t-stat are significant at 5% level. RIL, BPCL and MRPL did not show any significant impact of crude oil price volatility on their enterprise value as R2 value is less than 15%. The t-statistics reveal the significant direct

exposure of IOCL and HPCL enterprise value to crude oil price volatility since their functional and strategic activities are substantially influenced by changes in oil price during the past decade. On the other front, RIL, BPCL and MRPL were able to mitigate direct exposure of oil price volatility based on their raw material procurement policy, resources mobilization practices, operational diversification etc. Thus enterprise value of these firms is not only affected by crude oil price volatility but also by





other qualitative and quantitative factors which are not considered in this study. The serial correlation, heteroscedastic and normality tests were

insignificant, signifying proposed model’s ability to capture cause-effects between independent and dependent variables.

6.2.2.2 Impact of Crude Oil Price on Operating Profit Margin

Table No. 17 regression results between crude oil price & operating profit margin Test Particular RIL IOCL BPCL HPCL MRPL


R2 0.5870 0.0280 0.0030 0.0297 0.0650 F-statistic 11.3738 0.2305 0.0241 0.2454 0.5568

Prob. (F-stat) 0.0097** 0.6439 0.8803 0.6336 0.4768 t-Stat -3.3725 -0.4810 -0.1553 -0.4954 -0.7462 Prob. 0.0097** 0.6440 0.8804 0.6336 0.4769


F-stat 0.5460 0.4122 0.5431 0.0761 1.9723 Prob. F 0.6055 0.6796 0.6070 0.9275 0.2195 Obs*R2 1.5397 1.2082 1.5329 0.2476 3.9677

Prob. Chi-Sq. 0.4631 0.5466 0.4646 0.8835 0.1395


Test

F-stat 0.3747 1.8889 2.2024 1.6784 0.6930 Prob. F 0.5574 0.2066 0.1761 0.2313 0.4293 Obs*R2 0.4474 1.9101 2.1587 1.7342 0.7971

Prob. Chi-Sq. 0.5036 0.1669 0.1418 0.1879 0.3719



The above regression model indicate that RIL’s operating profit margin has been influenced to the extent of 60% due to crude oil price volatility. Other sample firms did not record significant impact of crude oil price volatility as R2 value is less than 10%. Since RIL has largest refining

facility as compared to its peers and the principal operation accounts for majority of revenue generation, thus influence of crude oil price fluctuations was clearly observed in case of RIL only but not in case of other sample firms.

6.2.2.3 Impact of Crude Oil Price on Net Profit Margin

Table No. 18 regression results between crude oil price & net profit margin Test Particular RIL IOCL BPCL HPCL MRPL


R2 0.3185 0.1679 0.0012 0.1755 0.0194 F-statistic 3.7404 1.6145 0.0097 1.7036 0.1588

Prob. (F-stat) 0.0891* 0.2395 0.9238 0.2280 0.7006 t-Stat -1.9340 -1.2706 -0.0985 -1.3052 -0.3985 Prob. 0.0892* 0.2396 0.9239 0.2281 0.7007


F-stat 0.1186 0.5857 0.9129 6.021 1.6042 Prob. F 0.8902 0.5856 0.4507 0.0368 0.2766 Obs*R2 0.3804 1.6335 2.3331 6.6746 3.4842

Prob. Chi-Sq. 0.8268 0.4419 0.3114 0.0355 0.1751


Test

F-stat 0.0008 4.3356 5.8693 7.4944 0.3841 Prob. F 0.9780 0.0709 0.0417 0.0255 0.5526 Obs*R2 0.0010 3.5147 4.2318 4.8368 0.4582

Prob. Chi-Sq. 0.9746 0.0608 0.0397 0.0279 0.4985



Table no. 18 represents regression model summary in which only RIL illustrated significant influence of crude oil price volatility on net profit margin. Independent variable was able to effect dependent variable to 1/3 level as RIL has been practicing economies of scale/scope in its principal operation

of oil refining business on pure commercial basis. Other sample firms have not disclosed considerable impact of crude oil price variations on their net profit margin as the influence was less than 20% since these firms are government owned and obtain subsidy from ministry of petroleum of India.





6.2.2.4 Impact of Crude Oil Price on Asset Turnover Ratio Table No. 19 regression results between crude oil price & asset turnover ratio

Test Particular RIL IOCL BPCL HPCL MRPL


R2 0.5878 0.1728 0.3283 0.0188 0.1923 F-statistic 11.4111 1.6717 3.9115 0.1538 1.9057

Prob. (F-stat) 0.0096*** 0.2321 0.0833* 0.7051 0.2047 t-Stat 3.3780 -1.2929 1.9777 -0.3922 -1.3804 Prob. 0.0097*** 0.2321 0.0833* 0.7051 0.2048


F-stat 1.7720 0.3881 0.7700 2.0977 0.3516 Prob. F 0.2485 0.6942 0.5039 0.2038 0.7171 Obs*R2 3.7134 1.1455 2.0426 4.1150 1.0490

Prob. Chi-Sq. 0.1562 0.5640 0.3601 0.1278 0.5918


Test

F-stat 3.7419 2.2982 0.4905 0.0922 2.2196 Prob. F 0.0891 0.1680 0.5035 0.7690 0.1764 Obs*R2 3.1868 2.2316 0.5777 0.1140 2.1719

Prob. Chi-Sq. 0.0742 0.1352 0.4472 0.7356 0.1406



Table no. 19 displays that RIL and BPCL were influenced by crude oil price volatility, efficiency ratio has revealed significant outcome. Referring table no. 9, HPCL and MRPL have recorded asset

turnover ratio of more than 300 but crude oil price volatility was not noteworthy as R2 value is less than 20%.

6.2.2.5 Impact of Crude Oil Price on Inventory Turnover Ratio

Table No. 20 regression results between crude oil price & inventory turnover ratio Test Particular RIL IOCL BPCL HPCL MRPL


R2 0.0018 0.3008 0.0000 0.0268 0.4994 F-statistic 0.0145 3.4430 0.0002 0.2204 7.9832

Prob. (F-stat) 0.9069 0.100 0.9883 0.6512 0.022** t-Stat 0.1205 -1.8555 -0.0151 -0.4695 -2.8254 Prob. 0.9070 0.100 0.9883 0.6512 0.022**


F-stat 0.6998 0.3149 0.1274 0.1751 0.1624 Prob. F 0.5331 0.7412 0.8827 0.8434 0.8537 Obs*R2 1.8916 0.9500 0.4074 0.5517 0.5135

Prob. Chi-Sq. 0.3884 0.6219 0.8157 0.7589 0.7735


Test

F-stat 0.8045 2.1562 4.0826 1.0101 1.8392 Prob. F 0.3959 0.1802 0.0780 0.3443 0.2121 Obs*R2 0.9137 2.1230 3.3789 1.1210 1.8693

Prob. Chi-Sq. 0.3371 0.1451 0.0660 0.2897 0.1716



Table no. 20 gives regression model summary wherein MRPL’s inventory turnover ratio was influenced to around 50% by crude oil price volatility. Other sample firms showed insignificant effect of crude oil price volatility on inventory turnover ratio. The empirical results obtained in this paper were similar to available literature on crude oil price volatility and financial performance of firm, there is no significant evidence of oil price risk in US and Japan, Hamao (1988) and Boyer and Fillion

(2007) indicates stock returns of Canadian oil and gas companies are positively associated with the appreciation of crude oil and natural gas prices. 7 FINDINGS Indian petroleum refining industry is 4th largest

in the world in terms of refining capacity. There are 23 refineries in India, out of which

18 are in public sector, 3 in private sector and 2 are joint ventures.





India is the largest exporter of petroleum products in Asia since August 2009.

Indian refineries have accorded top priority to reduce the energy consumption through various energy conservation measures.

Gross refining Margins at several refineries of Indian state-run firms are lower than those of private companies.

India imports more than two-thirds of its crude oil requirement, which constitutes 37% of total imports.

In FY16, crude oil import dependency is projected to come down as new domestic capacities come on line.

India will continue to be a net exporter of refined petroleum products and over the long-term (2035) it will become the largest source of global oil consumption & a net importer.

The combined effect of ARCH and GARCH in symmetric models validates persistent volatility that would endure in future periods too for a longer duration.

EGARCH model is negatively significant and it specifies presence of leverage effect and reports diverse impact of previous periods good and bad news on the volatility.

Diagnostic tests revealed insignificant results for all GARCH and EGARCH models and it proves the model fit prerequisites related to volatility modelling.

RIL had highest mean and maximum firm value among sample firms.

All sample firms are making adequate money from their operations to support the business activities and considered stable from its ongoing operations.

RIL was able to strategize product mix for higher net profit margin among sample firms.

All state-run firms have displayed better asset utilization capability and BPCL has been able to generate higher sales using all of its assets.

Impact of crude oil price volatility on IOCL and HPCL enterprise value is significant.

RIL’s operating and net profit margins have been influenced due to crude oil price volatility and economies of scale in principal operational activities

RIL and BPCL were influenced by crude oil price volatility and efficiency ratio has revealed significant outcome

8 CONCLUSION This paper has been developed to empirically analyze the impact of crude oil price volatility on firm value and financial performance indicators of Indian petroleum refining sector firms. Based on results it was realized that GARCH (1,1) and EGARCH(1,1) models with student’s t distribution were able to capture the symmetric and asymmetric

volatility estimates of crude oil price returns. The results of the paper supports the behavior of crude oil price variations observed during the past decade due to global economic crisis, geo-political unrest in MENA, natural calamities and most importantly commodity cycle bust. Univariate regression analysis revealed partial impact of crude oil price volatility on firm value and financial performance indicators of sample firms. Effects of crude oil price volatility were not uniformly significant across the sample firms since their principal operations were directly or indirectly influenced by ownership pattern, operational diversification, economies of scale/scope, exposure to international trade and other firm specific qualitative/quantitative factors. The empirical outcome of this study would be valuable to crude oil refining stakeholders who needs to identify the influence of crude oil price volatility before strategizing their future course of activities to protect the top and bottom lines of their enterprises. Finally, the results are also important to our country’s policy makers since our country depends on crude oil imports to fulfill the consumption requirements of domestic and commercial bodies. Future studies may explore the influence of other firm specific qualitative/quantitative factors on firm value and financial performance along with effects of oil price volatility related to Indian petroleum refining sector and other outstanding aspects are left for further empirical research. REFERENCES Afees A. Salisu & Ismail O. Fasanya, 2012,

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