1
Oil price and industry-specific returns: a
case study of India
In what way are Indian industrial stock returns affected by the Brent oil price?
Bachelor Thesis Finance & Organization
Tom Cornelissen 10608397 Supervisor: G. Vala Elias Pimentel Oliveira June 29, 2016
Abstract
This thesis examines the effect of the Brent oil returns on eight Indian industries. In the period of 2006-2016, evidence is found that the Brent returns move symmetrically with stock returns, the exception being the banking industry. The banking industry returns can be predicted using the oil returns. These findings support Nguyen and Bhatti (2008) conclusions for Vietnam.
2
Statement of Originality
This document is written by student Tom Cornelissen who declares to
take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original
and that no sources other than those mentioned in the text and its
references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the
supervision of completion of the work, not for the contents.
3
Table of contents
1. Introduction ...4
1.1Oil prices and stock returns ...4
1.2 Research ...5
1.3 Organization ...6
2. Literature ...7
2.1 Oil price and stock markets in developed markets ...7
2.2 Emerging markets ...7 2.3 Industry-specific returns ...9 3. Methodology ... 11 3.1 Dataset ... 11 3.2 CAPM... 11 3.3 VAR-model... 12
4. Effect of oil on industry returns ... 13
5. Conclusion ... 15
6. References ... 16
4
1. Introduction
1.1Oil prices and stock returns
This thesis intends to determine whether the oil returns significantly affect industry-specific returns. Previous research shows that oil prices have a significant effect on stock prices in developed
economies. Examples of these markets are European and the United States (U.S.) stock markets. Because these markets are researched to great extent, this paper focusses on the emerging economy of India. The main reason for choosing the emerging markets is based on several papers. Jain et al. (2006) state that Brazil, Russia, India and China will surpass developed economies within 50 years, if the forecasted growth rates continue to hold. Specific research on emerging economies is therefore essential as they account for a large part of the world economy.
India is part of the countries with huge growth potential, which has an immense effect on the oil consumption of the country (Basher and Sadorsky, 2006). Both the growth in their General Domestic Product (GDP) and the forecasted growth have increased the oil consumption between 1994 and 2004 by 80.9%. When evaluating the growth in GDP (Worldbank, 2015) between 2011 and 2015 (7.3% annually), the oil consumption should also increase significantly, according to Basher and Sadorsky’s findings. British Petroleum (BP) indeed states that oil consumption grew by 45.8% in total during the period of 2004-2014 (BP, 2015). The growth in consumption is accompanied by a surge in demand. This would indicate a certain exposure of the Indian economy, in the past years, to the price of oil. According to International Energy Association (IEA) oil demand in India will increase more than any other country (IEA, 2015b). The daily consumption of oil is predicted to surpass 10 million barrels per day by the end of 2040, a 168.3% increase of the daily consumption in 2014 (BP, 2015). As India produces and imports oil, imports need to increase to comply with demand. IEA proves this by stating that India’s oil production will only amount to 10% of the total demand in 2040 (IEA, 2015b), resulting in an increasing dependence on the import of oil. To enable compliance to the demand for oil in the future, India will need to secure $2.8 trillion in investment funds. In order to obtain this amount the Indian government will depend on a large scale of investors (IEA, 2015a). For example, the oil industry within the country will need to invest in order to increase its production. It will also play a role in the distributing and the refining of the imported oil. Investors in these
companies will only provide funds if the companies can provide significant returns. This portrays an example of the relation between oil and industry-specific returns.
Since oil is a limited resource and the worldwide oil prices vary depending on demand, it is likely that the stocks of certain industries are affected by oil price movements. Another reason for industry-specific research is based on the findings of Baffes (2007) and Elyasiani et al. (2011). The former concludes that oil price increases (decreases) have significantly different effects on each of the examined commodities. The latter concludes that oil price changes affect the returns of United States (U.S.) industries differently based on empirical evidence. Both empirical researches will be elaborated
5 in the following part of this paper. As an emerging market, Indian industries are different compared to the U.S. and thus will be affected differently by oil price changes. These changes are attributable to the shift of production of products from developed markets (U.S.) to emerging markets (India). As oil is mostly used in the production process and these processes are outsourced by most countries, different effects between the U.S. and the Indian industry analysis can be explained. The above stated facts indicate the relevance of the research problem which will examine the effect of oil price changes on the industry-specific returns of the Indian stock market.
1.2 Research
The main goal of this thesis is determining whether the volatility of the oil price significantly influences industry-specific returns, using a vector auto regression (VAR) model. This research focusses on the industry-specific returns of the main Indian stock exchange; the Bombay Stock Exchange (BSE). By emphasizing on the recent oil price changes, for example the oil crisis in 2014, this paper intends to establish a relationship between the oil price and the returns of different industries in India. Based on previous research on different countries, the oil price has a significant influence on stock prices. Whether this is applicable to Indian industry returns is determined by this research.
The way the analysis is conducted is as follows. As the industries are part of the overall BSE-index, we test whether this benchmark has a significant effect by running an ordinary least squares (OLS) regression. If this regression shows that the benchmark has a significant on all industries the industry-specific returns should be adjusted for the overall effect of the benchmark. After conducting this analysis, a time-series analysis is used to test whether there is a significant relation between oil price shocks and the industry-specific returns. The analysis most commonly used to determine
relationships between oil prices and stock variables is the Vector Auto Regressive (VAR) model. This is the main reason why this paper uses a VAR analysis as well.
This thesis intends to research the relation between industry returns and oil prices. It is presumed that exposure to oil shocks differs across industry. This is the main reason of analyzing different sectors. By using a VAR analysis we investigate whether industry return time-series are influenced by shocks in the oil price. This model is used because markets are often inefficient in processing information. Subsequently returns are possibly affected by lagged effect of the oil price. It also enables the estimation of a long-term model which enables forecasting.
In this thesis we find that most industries are only affected by the oil price to a small extend in the short-term. The effect of a shock in the Brent oil returns is most influential on the metal sector as the shock has a significant up to 3 days after its occurrence. We also find evidence that the Brent oil returns Granger cause banking stock returns and therefore might be useful in predicting these stock movements.
6
1.3 Organization
The remainder of this paper will be structured as follows. First the relevant literature concerning this thesis’ topic will be elaborated in part 2. The subsequent part will describe the dataset and explains the method used to answer the research question. Part 4 consists of the empirical results obtained from the benchmark model and the oil VAR analysis. Part 5 presents the conclusion and the limitations of this research. Part 6 shows the relevant tables and figures with the results of this research. Finally, the Appendix is separated in two sections (A and B) which are displayed in part 7.
7
2. Literature
Existing research on the relation between oil prices and stock market returns focusing on the
developed markets is evaluated in the first subparagraph. Following this subsection, the general effect of the oil prices on emerging stock markets is reviewed.
2.1 Oil price and stock markets in developed markets
According to Kilian and Park (2007) 22% of the U.S. stock volatility during the period of 1975-2006 is attributable to oil shocks. Using a Structural VAR model, their results show oil shocks generating varying real dividend growth rates and effects to the expected stock returns resulting from risk premiums. A relation between oil and stock performance is also found in the U.S., Japan, Canada and various countries in Europe (Jones & Kaul, 1996, Park & Ratti, 2008). Jones and Kaul (1996) use quarterly data to evaluate Canada, Japan, the United Kingdom and the United States. The relation between oil and stocks returns is caused by changes in the current and expected cash flow in both Canada and the U.S. (Jones & Kaul, 1996). However the effect of the oil prices on the stock markets of the United Kingdom and Japan are not caused by changes in cash flows. Park and Ratti (2008) conclude that between 1986 and 2005 the oil has had a significant effect on the real stock returns in the U.S. and 13 European countries. Even after excluding the effect of the U.S. stock market on the European markets the effect of the oil shocks still remains significant. According to Park and Ratti (2008) oil price shocks are accountable for 6% of the real stock return volatility.
More recent research from Bernanke (2016) finds a high correlation between both the stock market and the oil price to aggregate demand. It is argued that stock traders tend to react to oil price movements as if they are indicators of aggregate demand and economic growth. Another explanation to the symmetric movements between oil and stocks is the fact that investors retreat from both the stock markets as well as the commodity markets in times of high risk and uncertainty.
2.2 Emerging markets
The above stated research papers all provide evidence that developed markets have been investigated to great extent. Basher et al. (2012) provides evidence of the significance of the oil price changes on the overall stock markets within emerging economies. Previous research by Basher and Sadorsky (2006) also concludes that oil shocks are likely to have a significant influence on emerging economies and consequently on the stock returns. Oil prices affect stock prices and thus are an important factor in the stock markets. However, the result of their research is dependent on the frequency used in the datasets and it includes multiple emerging markets. A combination of daily and monthly data results in a positive relation between oil prices and excess stock returns, while weekly and monthly data provide a negative relation between these variables.
8 Emerging markets are characterized by their rapid economic growth which consequently increases demand for resources, thus including demand for oil. While these markets are increasing their oil consumption in order to develop their economies, developed markets are lowering their consumption by increasing their energy efficiency (Basher et al., 2012). The oil consumption in India has increased by 80.9% and other emerging markets shows growth rates in excess of 50%. However, the U.S. experienced a declining growth rate in their consumption of -10.4% between 1999 and 2009 (Basher et al., 2012). BP’s results show that the U.S. consumed 22% of the yearly global oil
consumption in 2009, but in 2014 they only consumed 19.9% (BP, 2015). This implicates that the future oil demand will be depending less on developed economies, like the U.S., and more on emerging economies. An example to justify this is the Chinese oil consumption surpassing the Japanese consumption. This resulted in China becoming the second largest oil consumer globally in 2009. The current projected growth rates of oil consumption and the fact that China, in 2014, already accounted for 12.4% of the total oil consumption (BP, 2015), will make it likely they will surpass the U.S. in the near future. Another emerging market consuming large amount of oil is India. India consumed a total of 4.3% making it the fourth largest global oil consumer, following Japan with respectively 4.7% (BP, 2015). As prices follow demand and the emerging markets are clearly increasing their demand it becomes more important to check for potential influences of oil on the economy. Potential influences on the stock markets of these markets for example. Research by Kilian and Park (2007) finds that the response of aggregate stock returns differ greatly depending on the cause of oil shocks. When an oil-market demand shock occurs, increasing the oil price, stock returns respond negatively. More than two-thirds of the U.S. stock volatility related to oil shocks, as Kilian’s results show, is attributed to the shock in demand for oil (Kilian and Park, 2007).
Park and Ratti (2008) also show that oil price shocks still attribute to 6% of the stock volatility in Europe. Specific research on the influence of oil prices on stock returns in emerging markets is still limited. Basher and Sadorsky (2006) use unconditional and conditional risk analysis to investigate whether there is a relationship between oil shocks and emerging market stock returns. Their findings in their conditional analysis state that oil price risk plays has a positive relation with the pricing of emerging market stock returns. The conditional risk analysis however provides a significant and negative relation between oil prices and emerging stock markets returns. The research includes 21 emerging markets so general conclusions might not reflect country’s specific situations. This is the main reason why this thesis focusses on a specific country as opposed to several countries.
However, certain papers also show that a link between stock returns and oil prices may be non-existent or oppose rational reactions. In the paper of Cong, Wei, Jiao and Fan (2008) a VAR model estimates the relationship between oil prices and stock movements in China. They find that oil price shocks do not have a significant effect on the market indices but they do find a significant positive effect on the manufacturing index and certain oil companies. However, the oil companies tend
9 to react negatively to important oil shocks. Another study examining Vietnam and China shows the same results for China as Cong et al (2008), but it also shows that the Vietnamese stock markets and the worldwide oil price move symmetric. A possible explanation for insignificant results of the analysis of China is the GDP growth in the examined period. The GDP growth could have absorbed the negative effects associated with oil price movements (Nguyen & Batthi, 2012). These examples indicate that industries operating in emerging markets, such as India, are not necessarily influenced by oil prices.
2.3 Industry-specific returns
This paragraph indicates the importance of including industry-specific results based on oil shifts as observed by previous researchers. Although the usage of oil in the production process differs across sectors and therefore returns are affected differently, most industries are affected by oil shocks due to huge array of by-products of oil in the overall economy.
Kilian and Park (2007) conclude that industry-specific responses to these shocks are more significant when caused by an increase in demand. This implies that different shocks affect industries differently and thus effects might vary across industries. Elyasiani et al. (2011) examine the effect of changes in current oil prices and oil future prices on different U.S. industries. The study examines whether 13 industry-specific returns are affected by shocks of both the oil and the future oil price and finds significant effects within nine of these industries. The research allocates the examined industries in four major sectors; oil-substitute, oil-related, oil-using and the financial sector. The main reason for the distinction between these sectors is to allow hedgers and investors with industry-specific portfolios to correctly anticipate the effect of fluctuations in the oil future price. They find that the oil-using sectors are mainly affected by oil price volatility and less by the volatility of the oil future price, while the reverse holds for the oil-substitute and oil-related industries (Elyasiani et al., 2011). The financial sector is influenced by both the oil price as well as the oil future price. Nandha and Faff (2008) also examine the relation between oil prices and industry-specific equity returns. Their study classifies industries as oil user or oil producer. In general, the relation between the oil price and the industry’s equity returns is found to be negative in most of the 35 sectors, with the exception of the mining, oil and the gas sectors. As stated by Baffes (2007), the commodities prices of both gas and metals tend to rise with increases in the oil price. This might be the reason for the exceptional cases found by Nandha and Faff. Their research (Nandha and Faff, 2008) suggest that the main reason that the oil price has a significant impact on most industries, including sectors such as banking and insurance, is the large amount of by-products of oil which are found all across economies; such as aviation fuel, shampoo and shoes. The effects however are strongly linked to the country’s total demand for oil.
10 Nandha and Faff (2008) argue that the influence of the oil price on the stock prices is
dependent on the pass-through effect. The pass-through effect is the ability of companies to pass on the higher oil prices onto their customers. For example, industries serving an intermediary role will see less effect on their profitability than industries serving consumers. Based on Baffes’ (2007) results the gas and oil industry will pass on the most of their costs on their customers which would decrease demand but might not lower profits. Basher et al. (2012) also mention the pass-through effect by stating that this effect will affect future product demand. Kilian (2008) provides several reasons as to why this effect might appear differently across industry sectors. The main reason behind these differences is the dependence of the consumers on oil-related products as Kilian (2008) suggests. For example, increases in energy prices do not affect demand for motorcycles or rental vehicles but it affects the demand for motor vehicles and leisure vehicles in the U.S. (Kilian, 2008). As mentioned before India is rapidly increasing its oil consumption (Basher et al. (2012), BP (2015)), so the overall dependence on oil is increasing. Positive demand shocks result in appreciation of the equity of the gold and silver mining industry, whereas the shares of the automobile and the retail sector show the opposite effect.
As stated, India is growing rapidly and so is its oil consumption. Several analyses show that oil and stock returns are related based on the research in developed countries. However, statistics show that India was the fourth largest oil consumer in 2014 (British Petroleum, 2015) and thus a country-specific analysis, especially in emerging markets, will provide valuable implications. The industry-specific research is relevant because it has been shown that sector-effects differ significantly. Also, sector analysis provides insights in possible profitable strategies for hedgers and investors when oil shocks occur.
11
3. Methodology 3.1 Dataset
The sample period for this analysis is from January 2, 2006 to April 15, 2016. This period is restricted by the availability of industry-specific returns. However it does provide insights as both the financial crisis of 2008 as well as the recent oil crisis (2014) are covered within the sample period. Due to these and other oil price fluctuations the period frame is deemed to be both relevant and insightful.
In this research the examined data is retrieved from Datastream. The dataset consist of the daily returns of the Bombay Stock Exchange (BSE), different industries and Brent crude oil price (Brent). These returns are calculated based on the prices available for each of these variables. The BSE 100 index measure is based on large-capital companies’ exchange of India and serving as a benchmark index. The different industries analyzed in this thesis are: the Automobile (BSEAUTO), Banking (BSEBANK), Consumer Durables (BSECNDB), Metal (BSEMETL), Oil and Gas (BSEO&GA), Power (BSEPOWR), Real estate (BSERELT), Technological (IBOMTEC) industry. These different sectors are based on the industry indices provided by S&P Dow Jones Indices, a division of S&P Global. Brent oil is listed as one of the mayor benchmarks by IEA (2014) for oil in general and is hence used as the oil benchmark in this thesis. Brent is also used as benchmark by Cong et al. (2008) in their analysis of the influence of oil on the returns of the Chinese stock market.
The retrieved data of each of the variables display the daily prices. These prices are
transformed to reflect the daily return, using the difference between the daily price and the price of the previous day. This step is also used by Cong et al. (2008) and is therefore applied in this research. The descriptive statistics and the correlations of the data are displayed in the table 1. These statistics initially show low means which indicates rather stationary variables. And, although the correlation between the industrial sectors and the Brent-price is significantly low, previous research has shown that the stock markets are affected by the oil price. The metalworking industry has the highest correlation with Brent (0.2475) which agrees with Baffes’ (2007) conclusions. All the sector show a high correlation with the BSE, which is expected as most companies in the underlying industries are part of this index.
3.2 CAPM
The first step in the analysis of the proposed problem is the applying the Capital Asset Pricing Model (CAPM). CAPM examines the relation between individual stocks and the index in which the stock is traded. Although the model does not include essential known patterns in the determination of stock returns (Fama & French, 2015), it still provides a simple model in which the overall market returns are added as an essential benchmark variable.
12 (1) 𝑅𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦− 𝑅𝑓 = 𝛽0+ 𝛽1(𝑅𝐵𝑆𝐸− 𝑅𝑓) + 𝜀
𝑅𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 is the daily industry-specific return of each of the sectors, 𝑅𝐵𝑆𝐸 is the daily return of the BSE and 𝑅𝑓 is the daily risk-free rate of India based on 10 year bond rates. The results of this analysis are displayed in table 2. The overall benchmark coefficients are highly significant across all the sectors with market exposure varying between 0.84 (Technology) and 1.35 (Real estate). Due to the high significance of the BSE variable and its impact on the underlying results, we determine that the industry-specific returns should be adjusted for the returns of the BSE. Therefore the VAR model will use returns adjusted to the BSE-returns.
3.3 VAR-model
The empirical analysis used to investigate the relationship between oil prices and industry-specific returns is a structural vector auto regression (VAR) model. Assuming the CAPM shows a significant effect of the market benchmark on the returns of the industries, we adjust the industry returns to this benchmark. This will enable us to take this effect into account without using it in our VAR. Therefore less degrees of freedom are lost in the analysis of the oil price and the industry returns. The following equation is used to determine the variables used in the VAR analysis:
(2) 𝑅(𝑖−𝑚)= 𝑅𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑡− 𝑅𝐵𝑆𝐸𝑡
Several previous studies use the VAR model in order to determine the effect of oil prices on stock returns (Kilian and Park, 2007, Cong et al., 2008). Kilian and Park (2007) state that alternative approaches to linking the oil price to stock returns are insufficient as they usually assume that other variables can be kept equal when changes occur. They conclude that omitting the ceteris paribus assumption explains the resilience in the U.S. stock market with respect to the oil price changes. But, it also explains the instability in the regressions concerning stock market variables and oil price changes.
Using a VAR analysis allows us to describe the behavior of the industry-specific returns time series to shock in the variables. This model uses equations of each variable in a linear form of its lagged values. The following equation (eq. 3) displays this;
(3) 𝑌𝑡 = ∑𝑝𝑖=1𝐴𝑖𝑌𝑡−𝑝+ 𝜀𝑡
In which 𝑌𝑡 is the change in the variable and 𝜀𝑡 is the disturbance in the variables. This disturbance is interpreted as a structural shock of the variables. Variable 𝐴𝑖 is the effect of the lagged factor on the current results. This basic form of the VAR model will use stationary variables; the industry-specific returns for each sector and the Brent oil price returns. Therefore all the variables are tested to
13 the oil price returns, the LM autocorrelation test is used. The LM autocorrelation uses a null
hypothesis which assumes no serial correlation between the error terms. Combining this with a lag length check enables us to determine which lag lengths fit the sectors. Following this the short-term model is estimated and the impulse decomposition is used to evaluate the short-term effect of oil on the industry returns. After this the variance decomposition is used to check whether the volatility of the industries can be explained by oil variable.
4. Effect of oil on industry returns
This section will describe the effect of the oil price on the industry-specific return time-series. Applying both the adjusted industry variable and the Brent daily return variable enables the VAR analysis of the influence of oil on the industry stock performance.
Figure 1 displays the time lapse of the variables used in this research. The stationarity is tested by applying a Dickey-Fuller test, with the results displayed in table 3. All of the variables display stationarity with significance at a 1% level. Secondly autocorrelation between the error terms needs to be ruled out. As autocorrelation will result in misinterpretation of the results, a combination of these tests is used to determine which lag lengths to use. Using lag length criteria of 8 day lags we conclude that the lag length used for each industry is a one-day lag, with the exception of the oil and gas industry (table 4). The oil and gas industry’s cost base is based on the energy prices and therefore efficiency in this market is expected. Therefore the non-existence of a lag was anticipated. The occurrence of the one-day lag in the other sectors can be explained by the fact that the Brent oil price is an international oil product and therefore is has different time zones in which it is traded. The U.S. and the Europe are ranked the most actively traded indices (Desjardins, 2016) and oil being the most actively traded commodity (Kowalski, 2016). Based on the time difference between these zones a one-day lag is expected. Another reason for a potential one-one-day lag is the common trends stock markets tend to experience.
The lagged effects appear to lack significance in some cases. However, Sims (1986) explains this occurs because the VAR-method does not accurately estimate the standard errors resulting in a distorted view of the results. Therefore the insignificance of these lags is deemed less important. This problem is resolved by examining the impulse response.
In order to estimate these impulse reactions the correct Cholesky order needs to be determined. In this analysis the Cholesky order starts with the industry-specific adjusted returns followed by the Brent oil returns. Based on the literature it is likely oil has an effect on the industry-specific stock returns (Kilian & Park, 2008) etc.). This is the case in the developed economies (Jones & Kaul, 1996; Park & Ratti, 2008) as well as the emerging markets (Nandha & Faff, 2008; Elysiani et al., 2011).
14 However, the effect a single industry in India has on the global oil price is deemed to be insignificant. According to BP (2015), India only consumes 5% of the total world demand in 2014. Although India is increasing its consumption yearly, this percentage is assumed too low for specific industries to have a significant effect on the oil price.
Figure 2a-h displays the impulse responses of each of the sector. As we only elaborate on the effects of the oil on the industry followed, these are the only graphs included in figure 2a-h. Based on figure 2a-h most of the industries react positively, one industry is influenced negatively and two industries do not react at all to unanticipated shocks in the oil. Five of these industries portray positive reactions to shocks in oil. However, only three industries show significant responses (at a 5%
significance level); banking, consumer durables and metal. Both the consumer durable and the metal sector returns show significant positive responses to the Brent oil price. The response of the former is applicable to the second day after the shock occurs, while the latter portrays a significant response in day 1, 2 and 3. Baffes (2007) also finds a positive relation between the commodity metal and the oil price. Bernanke (2016) states that positive relationships could be explained by a shift in aggregate global demand and investors treating the oil price as an indicator for economic growth. However, the banking sector reacts negatively and both the real estate and the technology sector do not show any reaction to a shock in the oil price at all. The unaffected sectors contradict existing literature because stocks normally either show a symmetric or an asymmetric reaction to shocks in the oil returns. The banking sector portrays the same effect as the industries examined in the
Following the impulse response evaluation we examine the variance decomposition to check if the variance of the industry-specific stock returns is affected by the variance in the oil returns. The results are shown in table 5. Based on these results we conclude that only the variance of the metal industry (3%) is affected by the oil return variance. Kilian and Park (2008) also find negligible results in their short-term results but the effects tend to increase when lengthening the time horizon. However, one would expect oil shifts to cause increases in the automobile, oil and gas and the power sectors’ variances. Unanticipated shocks would cause doubt among consumers buying cars, and with oil still being the common product of fuel, stock returns in this sector should experience increased volatility. The oil and gas and the power sector should be affected rather highly as both use oil as resources in their production processes.
Finally, the Granger causality is tested and displayed in table 6. Table 6 shows that the Brent returns Granger-cause stock returns in the banking, consumer durables and the metal sectors. These results are based on the one-day lag structure. Both the consumer durables and the metal sector also Granger cause the Brent returns and therefore do not allow predictions because the effect cannot be separated. This might be attributable to the fact that the GDP might be the underlying cause of the movements as Bernanke (2016) suggests. The most remarkable effect is the one-way Granger
15 causality between Brent and the banking industry-specific returns. This research concludes that the Brent oil returns are likely significant predictors of the returns the banking sector. Future research might show interesting effects of the Brent price on the returns of banks in India.
5. Conclusion
In this thesis, we investigate whether the oil returns have a significant effect on the returns of 8 industries in India. Previous research shows that the both symmetric and asymmetric effects are possible; therefore country-specific research is done to estimate the effect in India. The data of this research includes a recent time period (2006-2016) because of the volatility of Brent oil during this period. The examined industries are denominated by overall industry stocks in eight categories; the automobile, banking, consumer durables, metal, oil and gas, power, real estate and the technology sector. The VAR analysis shows that five industries move symmetrically when shocks in the Brent oil returns occur, with three industries showing significant positive effect. In the analysis only the banking sector shows a significant asymmetric relation to Brent oil. According to Nandha and Faff (2008) the overall appearance of different effects is explained by the varying amount of by-products of oil. Bernanke (2016) attributes effects to the oil price being an indicator of the GDP. We agree with this statement as oil has a large influence on today’s society and therefore is useful as indicator of overall price developments. However, we do find 2 industries that do not show any movement at all, namely the real estate and the technology sectors. This may be due to the daily interval used in the data, as monthly data shows more significant effect based on previous research. When evaluating the Granger causality, we find that the banking sector results could be predicted using the Brent oil returns as it shows a one-way causal effect.
In the future, we suggest that the number of industries evaluated is increased to reflect even more of the effect of oil returns on Indian stocks. We also recommend individual research to the effect oil shock have on the individual banking stocks as our research shows a one-way Granger causality between Brent on this sector’s stock returns.
16
6. References
Baffes, J.. Oil spills on other commodities. Resource Policy 32 (2007), pp. 126-134
Basher, S.A., and P. Sadorsky, Oil price risks and emerging stock markets. Global Finance Journal 17 (2006), p. 224-251.
Basher, S.A., A.A., Haug, & P. Sadorsky, Oil prices, exchange rates and emerging stock markets. Energy Economics, 34 (2012), p. 227-240.
Bernanke, B.S. The relationship between stocks and oil prices. Brookings Institute (2016). Retrieved from: http://www.brookings.edu/blogs/ben-bernanke/posts/2016/02/19-stocks-and-oil-prices British Petroleum (BP). The BP Statistical Review of World Energy 2015 (2015).
Chen, S.S.. Oil price pass-through into inflation. Energy Economics, 31 (2009), pp. 126-133.
Retrieved from:
https://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2015/bp-statistical-review-of-world-energy-2015-full-report.pdf
Chittedi, K.R.. International Review of Applied Financial Issues and Economics 2.1 (2010), pp. 18-36.
Cong, R.G., Wei, Y, Jiao, J., and Y, Fan. “Relationships between Oil Price Shocks and Stock
Market: An Empirical Analysis from China”. Energy Policy 36 (2008), pp.
3544-3553.
Desjardins, J. All of the World’s Stock Exchanges by Size (February 16, 2016). Retrieved
from:
http://money.visualcapitalist.com/all-of-the-worlds-stock-exchanges-by-size/
ECB. Economic and monetary developments. ECB monthly bulletin (October 2012). Retrieved from:https://www.ecb.europa.eu/pub/pdf/other/mb201210_focus05.en.pdf
El Hedi Arouri , M., D.K Nguyen. Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade. Energy Policy, 38 (2010).
Elyasiani, E., Mansur, I., & B. Odusami. Oil price shocks and industry stock returns. Energy Economics, 33 (2011), pp. 966-974.
Fama, E.F., K.R. French, A five-factor asset pricing model. Journal of Financial Economics 116 (2015), pp. 1-22.
George, R., & H. Breul. Benchmarks play an important role in pricing crude oil. EIA (2014). Retrieved from: http://www.eia.gov/todayinenergy/detail.cfm?id=18571
Huang, R.D., Masulis, R.W., H.R. Stoll. Energy Shocks and Financial Markets . Journal of Futures markets 16(1996).
International Energy Association. India Energy Outlook: World Energy Outlook special report. International Energy Association (2015a).
17 International Energy Association. World Energy Outlook 2015: Executive Summary. International
Energy Association (2015b).
Jain, Subhash C., ed. Emerging economies and the transformation of international business: Brazil,
Russia, India and China (BRICs). Edward Elgar Publishing (2006).
Jones, C. M., & G. Kaul. Oil and the stock markets. Journal of Finance 51(1996), pp. 463−491. Kilian, L..The Economic Effects of Energy Price Shocks. Journal of Economic Literature 46(2008),
pp. 871–909.
Kilian, L., & C. Park. The impact of oil price shocks on the U.S. stock market. International Economic Review, 50 (2008), pp. 1267-1287.
Kowalski, C. The most actively traded commodities (May 20, 2016). Retrieved from:
http://commodities.about.com/od/researchcommodities/a/most-liquid-commodity-markets.htm
Nandha, M., R. Faff. Does oil move equity prices? A global view. Energy Economics, 30 (2008), pp.
986–997
Nguyen, C.C., M.I. Bhatti. Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam. Journal of International Financial Markets, Institutions and Money, 22 (4) (2012), pp. 758–773
Park, J., R.A. Ratti. Oil price shocks and stock markets in the US and 13 European countries. Energy Economics, 30 (2008), pp. 2587–2608
Sims, C. Are forecasting models usable for policy analysis?. Federal Reserve Bank of Minneapolis Quarterly Review, 10 (1986), pp. 2–16.
Wang, Y., Wu, C. & L. Yang. Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries. Journal of Comparative Economics, 41 (2013), pp. 1220-1239.
Worldbank. Data: GDP growth (annual %). The World Bank (retrieved on 10 June 2016). Retrieved from: http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG
18
7. Appendix Section A: Tables
Table 1 Descriptive statistics
Mean Std. Dev. Min Max
Correlation with Brent Correlation with BSE BSE 0,0001834 0,0178314 -0,11314 0,203666 0.2353*** 1*** Brent 0,000103 0,0218353 -0,15388 0,196848 1*** 0.2353*** Auto 0,000353 0,0175061 -0,10722 0,146535 0.2149*** 0.8766*** Bankex 0,0003762 0,0225153 -0,12902 0,228785 0.1938*** 0.9188*** CD 0,00032 0,0201275 -0,12411 0,167852 0.1926*** 0.7604*** OG 0,000128 0,0202345 -0,15248 0,227942 0.2216*** 0.901*** Metal 7,39E-06 0,0246119 -0,13888 0,196972 0.2475*** 0.8788*** Power -0,0000582 0,0201794 -0,11712 0,219926 0.2049*** 0.9171*** Realty 0,0000905 0,0301607 -0,24631 0,272728 0.1734*** 0.8034*** Teck 0,0001387 0,0174662 -0,08936 0,175701 0.2043*** 0.8599***
*** denotes significance at 1 percent, ** denotes significance at 5 percent and * denotes significance at 10 percent
19 Table 2
Estimation results of the market benchmark model
Sector a B(market) Automobile Normal 0.0001951 (0.0001626 ) 0.8606366* ** (0.014044) Student t-test 1,2 61,28 Banking Normal 0.0001634 (0.0001713) 1.16019*** (0.0144211) Student t-test 0,95 80,45
Consumer Durables Normal 0.0001626
(0.0002526) 0.8582677*** (0.0186723) Student t-test 0,64 45,96 Metal Normal -0.000215 (0.0002263) 1.212974*** (0.0198357) Student t-test -0,95 61,15
Oil and Gas Normal -0.0000594
(0.0001695) 1.022431*** (0.0137309) Student t-test -0,35 74,46 Power Normal -0.0002486 (0.0001551) 1.037878*** (0.0116806) Student t-test -1,6 88,85
Real estate Normal -0.0001587
(0.0003462) 1.358937*** (0.0302891) Student t-test -0,46 44,87 Technological Normal -0.0000158 (0.000172) 0.8423062*** (0.0110457) Student t-test -0,09 76,26
*** denotes significance at 1 percent, ** denotes significance at 5 percent and * denotes significance at 10 percent
Table 3: Augmented Dickey-Fuller test
t-statistic Auto -49.28531*** Banking -46.62219*** Brent -50.99506*** BSE -48,15432*** CD -51.705*** Metal -47.73598***
Oil & Gas -51.49281***
Power -47.46161***
Real Estate -45.76946***
Technology -45.85283***
*** denotes significance at 1 percent, ** denotes significance at 5 percent and * denotes significance at 10 percent
20 Table 4: Recommended lag lenght per industry
Industries LR FPE AIC SC HQ
Auto 8 1 1 0 0
Banking 1 1 1 1 1
CD 1 1 1 0 1
Metal 8 1 1 1 1
Oil & Gas 5 0 0 0 0
Power 8 3 3 0 1
Real Estate 1 2 2 1 1
Technology 8 1 1 1 1
Note: The recommended lag length is given based on the following criteria; Likelihood Ratio Criterion(LR), Final Prediction Error Criterion (FPE), Akaike Information Criterion (AIC), Schwarz' Bayesian Information Criterion (SC), Hannan-Quinn Criterion (HQ)
21
Table 5: Variance decomposition
Peri
od
AU
TO
Bre
nt
BA
NKE
X
Bre
nt
CD
Bre
nt
MET
AL
Bre
nt
O_
G
Bre
nt
POW
ER
Bre
nt
REA
LTY
Bre
nt
TEC
K
Bre
nt
1
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
10
0.0
00
0
0.0
00
00
0
2
99
.97
28
4
0.0
27
16
2
99
.76
63
8
0.2
33
62
0
99
.53
41
3
0.4
65
87
4
96
.94
94
6
3.0
50
53
7
99
.95
22
5
0.0
47
74
7
99
.96
53
1
0.0
34
68
7
99
.99
67
4
0.0
03
26
1
99
.99
85
6
0.0
01
44
2
3
99
.97
27
3
0.0
27
27
4
99
.76
29
9
0.2
37
00
9
99
.53
39
8
0.4
66
01
7
96
.93
78
7
3.0
62
12
9
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
0
99
.99
66
8
0.0
03
32
2
99
.99
85
3
0.0
01
46
9
4
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
6
3.0
62
44
5
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
5
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
5
3.0
62
44
8
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
6
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
5
3.0
62
44
8
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
7
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
5
3.0
62
44
8
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
8
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
5
3.0
62
44
8
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
9
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
5
3.0
62
44
8
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
10
99
.97
27
3
0.0
27
27
4
99
.76
29
5
0.2
37
04
7
99
.53
39
8
0.4
66
02
1
96
.93
75
5
3.0
62
44
8
99
.95
22
4
0.0
47
76
5
99
.96
49
5
0.0
35
05
3
99
.99
66
8
0.0
03
32
3
99
.99
85
3
0.0
01
46
9
Not
e: a
ll th
e fig
ure
s ab
ove
are
per
cen
tage
s. Th
e fo
llow
ing Ch
ole
sky o
rde
r is a
ppli
ed;
ind
ustr
y re
turn
and
Bre
nt o
il re
turn
Auto
Ban
king
CD
Me
tal
Oil &
Gas
Po
wer
Rea
l Est
ate
Tech
nolo
gy
Tab
le 5
: th
e va
rian
ce d
eco
mp
osit
ion
22 Table 6: Granger Causality test results
Industry H0: Brent returns do not Granger cause industry returns
H0: Industry returns do not Granger cause Brent returns
Auto 0.73142 2.70627
Banking 6.35226** 0.05891
CD 12.5841*** 5.29775**
Metal 85.0668*** 3.97051**
Oil & Gas 1.28291 2.39184
Power 0.93894 0.24009
Real Estate 0.08891 1.30865
Technology 0.03930 0.37928
Note: the data displayed are the F-test results. *** denotes significance at 1 percent, ** denotes significance at 5 percent and * denotes significance at 10 percent
23 Section B: Figures -.06 -.04 -.02 .00 .02 .04 .06 06 07 08 09 10 11 12 13 14 15 16 A_M -.08 -.04 .00 .04 .08 06 07 08 09 10 11 12 13 14 15 16 B_M -.2 -.1 .0 .1 .2 06 07 08 09 10 11 12 13 14 15 16 BRENT -.08 -.04 .00 .04 .08 .12 06 07 08 09 10 11 12 13 14 15 16 CD_M -.08 -.04 .00 .04 .08 06 07 08 09 10 11 12 13 14 15 16 ME_M -.06 -.04 -.02 .00 .02 .04 06 07 08 09 10 11 12 13 14 15 16 OG_M -.050 -.025 .000 .025 .050 .075 .100 06 07 08 09 10 11 12 13 14 15 16 P_M -.15 -.10 -.05 .00 .05 .10 .15 06 07 08 09 10 11 12 13 14 15 16 R_M -.12 -.08 -.04 .00 .04 .08 06 07 08 09 10 11 12 13 14 15 16 T_M
Figure 1: Daily Returns of the Variables
-.002 .000 .002 .004 .006 .008 .010 1 2 3 4 5 6 7 8 9 10
Response of A_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
24 -.002 .000 .002 .004 .006 .008 .010 1 2 3 4 5 6 7 8 9 10 Response of B_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
Figure 2b: Response of the Banking industry to an oil shock
-.004 .000 .004 .008 .012 .016 1 2 3 4 5 6 7 8 9 10 Response of CD_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
25 -.004 .000 .004 .008 .012 .016 1 2 3 4 5 6 7 8 9 10
Response of ME_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
Figure 2d: Response of the Metal sector to an oil shock
-.002 .000 .002 .004 .006 .008 .010 1 2 3 4 5 6 7 8 9 10
Response of OG_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
26 -.002 .000 .002 .004 .006 .008 .010 1 2 3 4 5 6 7 8 9 10 Response of P_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
Figure 2f: Response of the Power sector to an oil shock
-.005 .000 .005 .010 .015 .020 1 2 3 4 5 6 7 8 9 10 Response of R_M to BRENT
Response to Cholesky One S.D. Innovations ± 2 S.E.
27 -.002 .000 .002 .004 .006 .008 .010 1 2 3 4 5 6 7 8 9 10 Response of T_M to BRENT
