THE EFFECT OF OIL PRICE VOLATILITY ON THE ISTANBUL STOCK EXCHANGE TRANSPORTATION INDEX 1
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1 THE EFFECT OF OIL PRICE VOLATILITY ON THE ISTANBUL STOCK EXCHANGE TRANSPORTATION INDEX 1 Murat Kıyılar 2, Serra Eren Sarıoğlu 3, Ebru Demirci 4 Abstract There have been extensive research with various econometric models taking a broad perspective to present the relationship between oil price and a country s macro economy. The relevant literature mainly focuses on developed regions like North America and Europe and is primarily concerned about the country-wide macroeconomic indicators such as GDP / GNP or with a narrower view, the stockmarket level in question. However, recent studies have revealed a relation between the movements in oil prices and current value of firms, which reflect the value of their anticipated future profits. Therefore, the present study with a novel approach examines the relationship between the oil price volatility and Transportation Index (XULAS) comprised of transportation firms listed in Istanbul Stock Exchange (ISE) of Turkey, a developing country. It uses monthly data from June 1997 to July 2010 with an econometric model to investigate the effect of oil price volatility on the XULAS covering the transportation industry, in which the movement of oil prices directly affects the financial performance of operating companies. The results are expected to enhance our understanding of the interaction between those variables and are of use to investors, managers and policy-makers. Keywords Oil price volatility, Stock returns, Transportation industry 1 Bu çalışma, VIII. Uluslararası Lojistik ve Tedarik Zinciri Kongresi nde, Maltepe Üniversitesi İstanbul da bildiri olarak sunulmuştur (Kasım 2010). 2 Murat Kıyılar, Istanbul University, Faculty of Business Administration, Finance Department, Avcılar, Istanbul, Turkey, muratkiy@istanbul.edu.tr 3 Serra Eren Sarıoğlu, Istanbul University, Faculty of Business Administration, Finance Department, Avcılar, Istanbul, Turkey, serraeren@istanbul.edu.tr 4 Ebru Demirci, Istanbul University, School of Transportation and Logistics, Avcılar, Istanbul, Turkey, edemirci@istanbul.edu.tr 1
2 INTRODUCTION Throughout the world history, oil has been one of the most crucial players in shaping the countries economical and political developments. In the post-world War II era, the series of crude oil price shocks have affected the world economy. The OPEC oil embargo of 1973 increased the price of oil by $10 per barrel and created economic and social crises in the developing countries. The second oil price shock was in 1979 and led to global recession. The third was in 1990 after Iraq s invasion of Kuwait. The prices rised by $10 per barrel again. The oil prices have grown up from $12 to $24 per barrel in 1999 in the last oil shock. Having in mind the importance of oil to the world economy, it is not surprising that there is a large body of literature studied the relationship between oil prices and macroeconomic variables such as economic growth, inflation, international debt and exchange rates. But in contrast, there exists a few papers that link oil prices to financial markets. Most of these papers deal with the data of the industrialized countries. Little research has been conducted on the effects of oil shocks in the developing stock markets. The contribution of this article is twofold. The main contribution to the literature is to study the impact of oil price changes on Turkish stock market, a developing financial market. As far as we are concerned, oil prices affect the industries with a relatively high proportion of their costs are oil-based inputs, especially Transportation sector. In this article, we conduct a detailed investigation of oil price effects to Transportation industry. This is the second contribution of the paper. The remainder of this article is organized as follows. The next section discusses the importance of oil in the Turkish economy and different sectors in Turkey, especially the transportation sector. The following section provides a review of the literature. The next section describes methodology and data. The Results section presents and interprets the empirical results. The last section concludes the paper with a discussion of the main implications of the findings as well as outlining the study s limitations and making suggestions for further work. OIL AND THE TURKISH ECONOMY Oil and oil related products have very big importance for world economy and politics. Economic development and growth are depending on consumption and production of oil. Both producing and consuming countries are aware of the impact of the fluctuation of oil prices. The balance of demand and supply of oil has important impact on the price of oil. In recent years, higher oil prices led to significant redistribution of global income from oil importers to oil exporters. Projected world oil consumption increases by 1.6 million barrels per day (bbl/d) in Countries outside the OECD, especially China, Saudi Arabia, and Brazil, represent most of the expected growth in the world oil consumption. Among the OECD countries, only the United States is expected to show significant increases in oil consumption of about 0.15 million bbl/d in both 2010 and Projected global oil consumption grows by another 1.5 million bbl/d in There has been a deep interest by researchers over recent years in the role of oil and other energy sources on financial markets. Energy prices, especially oil prices are likely to have important potential impact on the cost of factor inputs for many countries. Globally, the 1970 s were characterized by an increasing dependence of the economies on oil. Especially in 1973, there are large and unpredicted movements in the oil market and poor economic context especially in the USA. After big crisis, it is natural to investigate the potential links between oil prices and macroeconomic activities. In recent years, the big fluctuations of oil prices affect the oil importer countries 2
3 economies deeply. An oil price increase might also have a negative impact on consumption, investment and employment. Turkey is not a major oil producer, its emerging role is an important oil transit country. This strategic situation makes increasingly important to world oil markets. Turkey is one of the importer of energy products in the world and also has a growing economy. Turkey s economic growth is highly industry oriented. There is a growing demand for energy in the results of Turkey s spectacular economic growth. The demand for energy comes from energy related sectors such as construction and transport. You can see the total consumption and import of Turkey for the years on Tables 1 and 2. The increasing trend of oil consumption and imports declined in 2008 and 2009 because of the global crisis. The oil consumption is projected to increase in Turkey s total energy consumption depends on the oil, natural gas and electricity. Oil consumption is 44% of total energy consumption. It is expected that the oil price fluctuation in international markets might have an impact on the Turkish economy. TABLE 1 Total Consumption of Petroleum Products of Turkey (Thousand Barrels Per Day) Source: TABLE 2 Total Imports of Refined Petroleum Products (Thousand Barrels Per Day) Source: It is expected that the potential for a negative oil price sensitivity to be greatest in industries with a relatively high proportion of their costs devoted to oil based inputs, such as Transport. Oil price changes affect the ability of firms to decrease the cost of inputs. These firms should hedge against the oil price risk. Industries are not homogeneous so oil price fluctuation can have different influences. We expect a negative oil return sensitivity in the non-oil related industries where in oil price changes directly impact on cost. There should be negative oil price sensitivity at the transportation industry. Highway and Jet traffic have a rapid expansion since 1990 s in the world. And also private automobile ownership has been rising. Transportation sector is one of the growing industries in Turkey. Development of the Turkish transportation industry has led to a surge in demand for oil products especially fuel oil. The fluctuation of oil prices causes increasing the cost of fuel oil. Transportation Index (XULAS) is comprised of the stocks of Transportation industry. It consists of a few transportation companies. These companies are doing airline, transportation and logistics business. Transportation companies should take positions for hedging the oil price risk. Companies and investors should analyze oil price sensitivity of stock and index market returns. The aim of this paper is to analyze the oil sensitivity of the Turkish Transportation Stock market index returns. LITERATURE Although there has been a continuing interest by researchers in the impact that energy sources, especially oil, have on stock markets, the number of studies in this area is very limited. Besides, most of this limited papers work on the data of developed countries like US, UK, Canada and Japan. The number of studies dealing with developing countries is very restricted. 3
4 As we have mentioned before, oil prices started fluctuating in So this short time horizon leave little chance to researchers to have meaningful statistical inference about the effects of oil prices to economy. One of the earlier studies performed by [4] investigates whether oil price risk effects the prices in the stock markets. Actually, the researchers provided a test of a multi-factor asset pricing model using innovations in a set of macroeconomic variables. Oil prices were chosen as one of the independent variables in the study. But they found no evidence that oil prices are one of the factors constituting the stock prices in US equity markets. Reference [2] tests whether an oil price factor constitutes a systematic influence in the determination of prices in the equity markets of the US, Canada, Japan and UK. Reference [9] analyzes the impact of oil price changes in Canada, Japan, UK and US. They show that all the markets respond negatively to oil shocks. But the magnitude of the impact is substantially different across the four markets. The evidence suggests that the US and Canadian stock markets are rational: the reaction of stock prices to oil shocks can be completely accounted for by their impact on current and expected future real cash flows alone. In contrast, the evidence for Japan and UK is puzzling. In both countries, the researchers are unable to explain the effects of oil price shocks on stock returns using changes in future cash flows and/or financial variables. While [9] uses quarterly data, [8] considers daily data on the oil futures market and the stock market, and estimates a vector autoregressive model in order to determine the effect of oil shocks to futures and spot markets of US. The researchers find an evidence of a connection between oil futures returns and oil stock returns and stock prices are negatively impacted by rises in oil and gas prices. Reference [11], using the same methodolgy of [8] and including US industrial production and short interest rates to the model, separates positive form negative oil shocks. Both shocks affect aggregate stock returns but positive oil shocks are of large importance, whereas negative ones have little or no effect. Reference [7] investigates the sensitivity of Australian industry equity returns to an oil price factor over the period The researchers find significant positive oil price sensitivity in the Oil and Gas and Diversified Resources industries. Similarly they find negative oil price sensitivity in the Paper and Packaging, and Transport industries. Reference [12] provides a detailed analysis of the relationship between oil prices and equity values in the Canadian oil and gas sector, using monthly data covering the period from the final quarter of 1983 to the final quarter of [12] reports a significant positive relationship between the oil and gas equity index and the price of crude oil. Reference [10] finds that expected changes in the oil price are able to predict relative sector performance in US. Reference [5] tests whether changes in oil prices predict stock returns. The researchers use data of 48 countries, a world market index and price series of several types of oil. Stock returns tend to be lower after oil price increases and higher if oil prices decline in the previous month. They use thirty-year sample of monthly data for developed stock markets but the time horizon was shorte in emerging markets. Reference [6] investigates the relationship between the price of crude oil and equity values in the oil and gas sector using data relating to the UK. The evidence indicates that the relationship is always positive, 4
5 often highly significant and reflects the direct impact of volatility in the crude oil on share values within the sector. Reference [3] using a multifactor arbitrage pricing model, finds strong evidence that oil price risk impacts returns of emerging stock markets. Reference [1] uses developed countries data (Japan, Norway, Sweden, the UK and the US) to present an empirical study of volatility spillover from oil prices to stock markets within an asymmetric BEKK model. They find strong evidence of volatility spillover for all stock markets but the Swedish one, where only weak evidence is found. News impact surfaces show that, slthough statictically significant, the volatility spillovers are quantitatively small. The stock market s own shocks are more prominent than oil shocks. DATA AND METHODOLOGY The aim of this research is to find the impact of oil price volatility to the Transportation sector. As a matter of fact, the Brent crude oil prices are used to determine the oil price volatility. For the Transportation sector we use the Transportation Index (XULAS) in Istanbul Stock Exchange. The sample period for the XULAS index and Brent crude oil price is monthly and covers the period July, to July, resulting in 163 observations. The reason why starting from 1997 is that available return data XULAS index is beginning from July In Figure 1, you can see the trend of XULAS returns and Brent crude oil returns. FIGURE 1 The Trend of XULAS Returns and Brent Crude Oil Returns RESULTS In this paper, ARCH and GARCH models are used in order to calculate the volatility of Brent crude oil. Before ARCH and GARCH models, we first test if Brent crude oil has unit root which in turn to determine whether we need to estimate the equations in the first differences instead of levels. There are two different methods for testing stationarity. One of them is to graph the correlogram of the coefficient of autocorrelation of the series. The second method is to test ADF unit root test statistics value for series. In this paper, we test ADF statistics values for Brent crude oil prices at level and first differences. As can be seen from Table 3, we conclude that Brent oil series is no stationary at the level but at the first difference. Absolute ADF test statistics value is greater than test critical values. 5
6 Level TABLE 3 ADF Unit Root Test Statistics First Difference L Constant Trend&Constant L Constant Trend&Constant * * ** ** * Critical Values for %5 are; and and for %1 are; and **Critical Values for %5 are; and and for %1 are; and Brent crude oil series is stationary in the first difference at the 5% and 1% level. Therefore we conduct the analysis in terms of oil price changes. Until a decade ago the focus of most macroeconometric and financial time series modeling centered on the conditional first moments, with any temporal dependencies in the higher order moments treated as a nuisance. The increased importance played by risk and uncertainty considerations in modern economic theory, however, has necessitated the development of new econometric time series techniques that allow for the modeling of time varying variances and covariances. Given the apparent lack of any structural dynamic economic theory explaining the variation in higher order moments, particularly instrumental in this development has been the autoregressive conditional heteroskedastic (ARCH) class of models introduced by Engle (1982). ARCH and GARCH models are important for many issues in macroeconomics and finance, such as irreversible investments, option pricing, the term structure of interest rates, and general dynamic asset pricing relationships. Also, from the perspective of econometric inference, the loss in asymptotic efficiency from neglected heteroskedasticity may be arbitrarily large and, when evaluating economic forecasts, a much more accurate estimate of the forecast error uncertainty is generally available by conditioning on the current information set. In Figure 2, there exists big fluctuation and volatility clustering at the Brent crude oil returns. The big differences follow big movements and small differences follow small movements. This is the sign of volatility. FIGURE 2 The Trend of Brent Oil Return 6
7 After analyzing the unit root test we should estimate the ARMA models that best fit the Brent crude oil series. As you see from Table 4, AR(1) model is chosen according to Akaike Criteria Information. Besides coefficient of c and AR(1) is significant. TABLE 4 AR (1) Model Test Results Variable Coefficient Std. Error t-statistic Prob. C AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) We test the ARCH effect with the ARCH-LM test. We reject the null hypothesis, H n There is ARCH effect at the Brent Crude Oil. TABLE 5 Heteroskedasticity Test: ARCH F-statistic Prob. F(1,159) Obs*R-squared Prob. Chi-Square(1) In order to eliminate the ARCH effect we should choose the best ARCH-GARCH model. After trials of the ARCH and GARCH models, we chose the best fit model for Brent crude oil GARCH (1,1) model according to Akaike Information Criteria. Model results can be seen from Table 6. 7
8 TABLE 6 GARCH(1,1) Model Variable Coefficient Std. Error z-statistic Prob. C AR(1) Variance Equation C RESID(-1)^ GARCH(-1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) series. The ARCH LM test for GARCH(1,1) can be seen from Table 7. There is no ARCH effect in the TABLE 7 Heteroskedasticity Test: ARCH for GARCH(1,1) F-statistic Prob. F(5,151) Obs*R-squared Prob. Chi-Square(5) Afterwards we form the GARCH(1,1) series for every month. In order to determine the relationship between oil price volatility and transportation index return, a regression analysis is employed: R it ( Oil Pr. Volatility ) it R it =Return on Transportation index in month t The equation found is as follows: R it = 0, ,033β it 8
9 As can be seen from the equation, there is a positive relation between oil price volatility and index returns. But the results of the test imply that the relationship is not significant. Also the R 2 value is very low: % 0,0011. When a correlation analysis is applied to the data series, a correlation coefficient of 0,034 is found at 10 % significance level. CONCLUSION Energy prices in general and oil prices in particular are likely to have an important affect on the costs of many companies. Oil prices affect the industries with a relatively high proportion of their costs are oilbased inputs, especially Oil and Gas, Paper and Packaging and Transportation sectors. In this paper, we investigate the sensitivity of Turkish transportation industry index returns to an oil price volatility over the period The oil price volatility is modeled by GARCH(1,1). In order to find the relation between oil price volatility and transportation index we conduct a regression and a correlation analysis. The evidence suggests that there is no significant relation between the variables. The correlation coefficient is found to be 0,034 with a positive sign. Actually, the negative relation between oil price volatility and transportation index is expected. There are many reasons of the insignificant relation between the variables. Firstly, XULAS involves restricted number of transportation firms. Only 4 firms can not explain the relationship between the variables. When analyzing sensitivity of energy price changes to the transportation sector, transportation index is not sufficient to indicate the effect of energy price fluctuation. The impact of oil price changes on equity prices depend on the firms ability to hedge the price risk without changing goods and services price for customer. Second, different variables can impact industry returns in disparate ways. Moreover, the stock market s own shocks, which are related to other factors of uncertainty than the oil price, are more effective than the oil price shocks. REFERENCES [1] Agren, M., 2006, Does Oil Price Uncertainty Transmit to Stock Markets?, Working Paper, Uppsala Universitet, Department of Economics, 23, pp [2] Al-Mudhaf, A. and Goodwin, T.H., 1993, Oil Shocks and Oil Stocks: Evidence From The 1970s, Applied Economics, 25, pp [3] Basher, S.A. and Sadorsky, P., 2006, Oil Price Risk and Emerging Stock Markets, Global Finance Journal, 17, pp [4] Chen, N., Roll, R. and Ross, S.A., 1986, Economic Forces and the Stock Market, Journal of Business, Vol. 59, No. 3, pp [5] Driesprong, G., Jacobsen, B. and Maat B., 2004, Stock Markets and Oil Prices, [6] El-Sharif, I., Brown, D., Burton, B., Nixon B. and Russell, A., 2005, Evidence On The Nature And Extent Of The Relationship Between Oil Prices and Equity Values In The UK, Energy Economics, 27, pp [7] Faff, R.W. and Brailsford, T.J., 1999, Oil Price Risk and the Australian Stock Market, Journal of Energy, Finance and Development, 4, pp [8] Huang, R.D., Masulis, R.W. and Stoll H.R., 1996, Energy Shocks and Financial Markets, The Journal of Futures Markets, Vol. 16, No. 1, pp
10 [9] Jones, C.M. and Kaul, G., 1996, Oil and the Stock Markets, The Journal of Finance, Vol. 51, No. 2, pp [10] Pollet, J.M., 2003, Predicting Asset Returns With Expected Oil Price Changes, Working Paper, Harvard University. [11] Sadorsky, P., 1999, Oil Price Shocks and Stock Market Activity, Energy Economics, 21, pp [12] Sadorsky, P., 2001, Risk Factors in Stock Returns of Canadian Oil and Gas Companies, Energy Economics, 23, pp
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