The Relationship between Stock Returns, Crude Oil Prices, Interest Rates, and Output: Evidence from a Developing Economy

The Empirical Economics Letters, 5(4): (July 2006) ISSN 1681 8997 The Relationship between Stock Returns, Crude Oil Prices, Interest Rates, and Output: Evidence from a Developing Economy Ramazan Sari Department of Economics, Abant Izzet Baysal University 14280 Golkoy, Bolu, Turkey Ugur Soytas Department of Business Administration, Middle East Technical University 06531 Ankara, Turkey E-mail: soytas@ba.metu.edu.tr Abstract: In this paper we investigate the impact of oil price shocks on the macroeconomy of a developing country, Turkey. In the literature, a number of studies examine the affects of the innovations in oil prices in developed countries. Their results suggest that the oil price changes are important factors in explaining the variation in GDP, inflation, employment, and real stock returns. Only recently, the same effect has been investigated for developing countries. To the extent of our knowledge, no study has examined this relationship for Turkey. Our results seem to differ from the literature in that oil price shocks do not appear to have a significant impact on real stock returns in the Istanbul Stock Exchange. Keywords: Oil Prices, Variance Decompositions, Stock Returns, Impulse Responses, Macroeconomic Variables Classifications: E1, G1, Q4 1. Introduction Considerable attention has been given to the relationship between oil price shocks and macroeconomic variables in the energy economics literature. Both the macroeconomic consequences of the oil price shocks and the channels through which the shocks influence the economy have been the main subjects of several studies. Furthermore, the link between oil prices and macroeconomics through the financial markets has recently been explored in a growing body of research. However, there seems to be no consensus on the type and direction of the relationship between financial variables and changes in oil prices. Corresponding author. Email: sari_r2@ibu.edu.tr

The Empirical Economics Letters, 5(4): (July 2006) 206 The pioneering work of Hamilton (1983) shows that there is a significant correlation between oil prices and the US output. He argues that the fact that oil price shocks precede all of the US economic recessions (except the one in 1973) calls for a causal interpretation of the significant correlation between oil prices and output. A substantial literature on oil prices and some macroeconomic indicators evolved after his findings (see Hamilton (2000), and Jones et al (2004) for a comprehensive review of this literature). There are several studies that report significant affects of oil price shocks in developed economies, mainly the US economy. Burbidge and Harrison (1984) analyze the affects of oil price shocks on several macroeconomic indicators for the US, Canada, Japan, Germany, and the UK. They confirm Hamilton s results for US and UK, but report relatively smaller affect on industrial output for the others. Focusing on the US economy, Gisser and Goodwin (1986) also show that oil price changes have important affects on output, price level, unemployment, and real investment. Using a different framework and extended data, Mork (1989) and Brown and Yucel (1999) confirm Hamilton s results for the US. Mork et al (1994) study the oil price, GDP relationship in seven OECD countries. They find significant negative correlation for the US, Canada, Japan, Germany, France, and the UK, but positive significant correlation for Norway. Lee et al (1995) show that a shock to oil prices has a larger impact on an economy with a relatively stable history of oil prices prior to the shock. Ferderer (1996) indicates that the US macroeconomic indicators are affected not only by changes in oil prices but by changes in oil price volatility. Huntington (1998) argues that shocks to crude oil prices may not be reflected symmetrically through the energy system; however, they still have significant impact on the US GDP. Using a general equilibrium model de Miguel et al (2003) show that oil price shocks are important as sources of economic fluctuations in Spain. They also find that increases in relative oil prices result in a decline in welfare. Most of the works cited above focus on identifying the channels through which oil price changes affect the economy as well as the asymmetric effects of the innovations in oil prices (see Jones et al (2004) for a more detailed review) 1. Recent studies are interested in the affects of oil price changes on financial markets in developed countries. Kaul and Seyhun (1990) note a negative significant relationship between real stock returns in the NYSE and oil price volatility. Jones and Kaul (1996) 1 The literature in the subject area proposes several channels that account for the relationship between oil price shocks and macroeconomic activity. See Ferderer (1996) and Mork et al (1994) for a detailed review and discussion of these channels.

The Empirical Economics Letters, 5(4): (July 2006) 207 study the responses of stock markets in the US, Canada, Japan, and the UK to shocks in oil prices. Their results indicate that in the US and Canada, changes in stock prices can be completely justified by the impact of oil price shocks on real cash flows. On the other hand, the reactions of stock prices in Japan and the UK to innovations in oil prices are too large to be completely accounted for by the real cash flows alone. Huang et al (1996) show that changes in daily oil futures returns affect individual company stocks, but do not have significant impact on aggregate market indices in the US. Sadorsky (1999) argues that both oil price changes and the volatility of oil prices are important factors affecting US real stock returns. Indeed he finds that interest rates explain a smaller portion of the forecast error variance of stock returns than oil price changes. The literature on the oil price-stock returns suggest that the importance of the information contained in oil price shocks for financial markets may be different even for developed economies. To the extent of our knowledge, there are not too many studies on the impact of oil price shocks in developing country stock markets. In a more recent study on Greece, Papapetrou (2001) shows that oil prices have significant affects on real economic activity and employment. She also shows that a positive shock to oil prices decreases the stock market returns. Whether innovations in oil prices have similar interpretations in other developing countries or not needs further investigation. In this paper, we examine the relationship between changes in oil prices and the real returns of the stocks traded in the Istanbul Stock Exchange (ISE), interest rates, and industrial production in Turkey. Turkey experienced high level of inflation more than thirty years without running into hyperinflation. The high variability in prices was associated with high volatilities in macroeconomic variables. Due to this unique feature of the country, one can asses some interesting relationships between macroeconomic variables otherwise could not be detected for other countries which have low variability in macroeconomic variables (see for example Sari and Soytas, 2005). Changes in oil prices are expected to have important impacts in the Turkish economy, since the country is a net energy importer (68% of total primary energy consumption in 2002) and oil accounted for about 40% of her primary energy consumption in 2002 (State Planning Organization, 2004). Thus, oil price shocks may contain important information for traders in the ISE as well as policy makers. To the extent of our knowledge there are no studies on Turkey that examine the impact of oil price changes on the macroeconomic variables employed in this paper.

The Empirical Economics Letters, 5(4): (July 2006) 208 We employ generalized forecast error variance decomposition and impulse response analysis (Koop et al. (1996), and Pesaran and Shin (1998)). It is discussed enough in the literature that all the works that utilize Cholesky decomposition are subject to the orthogonality critique of Lutkepohl (1991). The standard variance decomposition and impulse response analyses use Cholesky decomposition to orthogonalize the shocks, and therefore, the results are sensitive to the order in which the variables are entered into the VAR. The generalized method overcomes this problem, since the results obtained by the generalized approaches are insensitive to the order of variables in the system. This paper proceeds as follows. Section 2 introduces the data and the methodology. Section 3 presents the empirical evidence and section 4 concludes. 2. Data and Methodology This paper utilizes generalized forecast error variance decomposition and generalized impulse response technique of Koop et al. (1996), Pesaran and Shin (1998). Forecast error variance decompositions is able to identify the relative importance of an individual variable in generating variations in its own and in the other variables. The following discussion is rephrased from Cheung and Yuen (2002). Consider the following VAR representation for w 2 t : p w t = B ϕ w t i + ε t (1) i where w t is a mx1 vector of jointly determined endogenous variables, ϕ 1 through ϕ p are mxm matrices of coefficients to be estimated, t is linear time trend, B is a vector of constant and ε t is a well-behaved disturbances with covariance Σ = σ ij. The generalized impulse response of w t+n with respect to a unit shock to j-th variable at time t is represented by (D n Σe j )( σ ij ) -1, where D n = ϕ 1 D n-1 + ϕ 2 D n-2 + + ϕ p D n-p, n = 1,2,, D 0 = I, D n = 0 for n < 0, and e j is mx1 selection vector with unity as its j-th element and zero elsewhere. Then, generalized forecast error variance decompositions can be computed by 2 A more technical explanation of the generalized approach is can be found in Pesaran and Shin (1998). A text book treatment of the standard methodology can be found in Hamilton (1994).

The Empirical Economics Letters, 5(4): (July 2006) 209 n 1 ' 2 σ ij ( eidlσe j ) l= 0 n ' ' ( e DlΣBD ei ) i l l= 0 2 The generalized approach generate similar results as the orthogonalized approach, if the covariance matrix of the errors is not contemporaneously correlated in individual equations comprising the VAR. That is the case if the covariance matrix is diagonal or almost diagonal. We check this by employing the log-likelihood ratio test (LR). The null hypothesis is that the off-diagonal elements in the covariance matrix equal zero, against the alternative hypothesis that at least one of the off-diagonal elements have a non-zero value. The log-likelihood ratio test statistic is calculated as LR = 2(LL u LL r ) and has a χ 2 distribution. Our findings suggest that the LR test statistics reject the null hypothesis at 1% significance level. Thus, the use of generalized approach is appropriate. We use monthly data on the following variables for the period of 1987:01-2004:03. The period is chosen on the basis of data availability. Different types of data series are used for oil prices in the literature. Following Hamilton (1983), Gisser and Goodwin (1986), and Sadorsky (1999), we use Crude Oil wholesale price index 3 deflated by consumer price index (CPI) for oil prices (ROILP). As an alternative series, we also use West Texas Intermediate Spot Oil Price as a check of robustness. We employ Istanbul Stock Exchange indices (ise) to calculate the stock returns by ln(ise t /ise t-1 ). Then, following Papapetrou (2001), we compute the real stock returns (ORSR) as the difference between the stock returns and inflation rate (calculated using CPI). In a developing country, the series that can be used for interest rate in a time series analysis is limited. We utilize the 12-month interest rate (IR12M) as in Papapetrou (2001). Finally, as an output variable, industrial production index (IP1997) is used 4. (2) All variables are in natural logs except real returns due to negative values in the original data. West Texas Intermediate Spot Oil Prices (Dollars per Barrel) is obtained from FRED II 5 database. The rest of the data is sourced from The Central Bank of Turkey 6. 3 Analogous to the producer price index in the US. 4 We could not use an employment related variable in our analysis due to the unavailability of monthly employment data for Turkey. 5 http://research.stlouisfed.org/fred2/

The Empirical Economics Letters, 5(4): (July 2006) 210 The stationarity properties of the series are checked via Augmented Dickey-Fuller (1979) (ADF), and Kwiatkowski-Phillips-Schmidt-Shin (1992) (KPSS) unit root tests. The results of the unit root tests are summarized in Table 1. Table 1: Unit Root Test Results PANEL A. Levels ADF KPSS ORSR Intercept -5.2173* (14, AIC) -5.2173* (14, G-to-S) 0.0506 Intercept and trend -5.2470* (14, AIC) -5.2470* (14, G-to-S) 0.0289 IP1997 Intercept -0.4930 (13, AIC) -0.4930 (13, G-to-S) 1.7151* Intercept and trend -2.5327 (13, AIC) -2.5327 (13, G-to-S) 0.2420* IR12M Intercept -0.7184 (1, AIC) 0.0852 (14, G-to-S) 0.4325*** Intercept and trend -1.0975 (1, AIC) -0.2567 (14, G-to-S) 0.3535* ROILP Intercept -3.3071** (1, AIC) -3.0648** (11, G-to-S) 0.3339 Intercept and trend -3.4569** (1, AIC) -3.2889*** (11, G-to-S) 0.2158** PANEL B. First Differences ADF KPSS IP1997 Intercept -4.4235* (14, AIC) -4.4235* (14, G-to-S) 0.1160 Intercept and trend -4.3900* (14, AIC) -4.3900* (14, G-to-S) 0.1163 IR12M Intercept -10.5380* (0, AIC) -4.9791* (13, G-to-S) 0.3674*** Intercept and trend -5.6407* (14, AIC) -5.4498* (13, G-to-S) 0.0512 ROILP Intercept -7.8564* (4, AIC) -4.3892* (14, G-to-S) 0.0390 Intercept and trend -7.8434* (4, AIC) -4.3203* (14, G-to-S) 0.0406 All variables seem to be I(1) except ORSR. Hence, the VAR system is estimated with ORSR in levels and others in first differences. 6 http://www.tcmb.gov.tr

The Empirical Economics Letters, 5(4): (July 2006) 211 3. Variance Decomposition and Impulse Response Results 3.1 Variance Decompositions The results for the variance decompositions analysis are reported in Table 2 7. A 10-month horizon is chosen for the analyses. This horizon is enough to capture the size of the effects, when the parameters converge to a steady state value. Unlike the orthogonalized case, the row values for the generalized decompositions do not have to sum to 1, because the generalized approach give the amount of forecast error variance decomposition for each variable like an average. Table 2: Generalized Forecast Error Variance Decomposition Panel A. Dependent variable: DORSR Horizon DORSR DLIR12M DLIP1997 DLROILP 0 1000 0.13532 0286 0588 1 0.95172 0.12580 0.01025 0.05313 2 0.93207 0.15613 0908 0.05614 3 0.92191 0.15569 0.01905 0.05546 4 0.91788 0.15473 0.01916 0.05516 5 0.91005 0.15127 0.02588 0.05411 10 0.89681 0.15186 0.03019 0.06210 Panel B. Dependent variable: DLIR12M Horizon DORSR DLIR12M DLIP1997 DLROILP 0 0.13532 1000 0079 0032 1 0.14678 0.99252 0236 0378 2 0.14473 0.97827 0327 0.01730 3 0.14449 0.97645 0330 0.01937 4 0.14530 0.93640 0.03351 0.02043 5 0.14412 0.92174 0.04280 0.02109 10 0.14366 0.90989 0.04823 0.02528 7 We also tested for cointegration. Since real stock returns data were I(0), we employed the analyses with I(1) variables. We could not find evidence of cointegration between the variables. These findings justify the use of differenced data in VAR. The results are available upon request.

The Empirical Economics Letters, 5(4): (July 2006) 212 Table 2 continued Panel C. Dependent variable: DLIP1997 Horizon DORSR DLIR12M DLIP1997 DLROILP 0 0286 0079 1000 0000 1 0234 0148 0.98450 0.01453 2 0371 0696 0.97368 0.01975 3 0.01014 0.01316 0.96230 0.02248 4 0.01006 0.01312 0.95672 0.02763 5 0.02469 0.01393 0.94143 0.02713 10 0.04357 0.02226 0.91954 0.02902 Panel D. Dependent variable: DLROILP Horizon DORSR DLIR12M DLIP1997 DLROILP 0 0588 0032 0000 1000 1 0.01461 0175 0173 0.99180 2 0.01879 0429 0645 0.98269 3 0.02085 0430 0.01643 0.96891 4 0.02383 0454 0.01838 0.96499 5 0.02453 0541 0.02046 0.96140 10 0.02821 0.01068 0.02764 0.94667 Lag lengths are in parentheses. AIC, and G-to-S refer to Akaike information criterion, and the general to specific method, respectively. The G-to-S methodology starts with a maximum allowable lag length of 14, decreases lags by one based on the t significance test and stops when the lag is significant. *, **, *** represent significance at 1%, 5%, and 10% respectively. The null of ADF and PP tests are of non-stationarity, whereas that of KPSS is stationarity. The impact of interest rates, oil prices and industrial production on real stock returns are reported in Table 2. The results reveal that the highest initial variability in real stock returns is attributed to shocks in itself. Interest rate has the second highest initial impact (more than 13%). After the first month, interest rate accounts for more than 15 percent of the variance in stock returns and explains approximately the same amount by the 10 th horizon.

The Empirical Economics Letters, 5(4): (July 2006) 213 Although oil prices do not have a considerable initial impact on returns, they explain more than 5 percent of the forecast error variance of real stock returns by the end of the 1 st month. By the 10 th month, the impact of oil prices reaches approximately 6.2 percent. Industrial production has the lowest impact on returns throughout all horizons. Relative to their impact on real stock returns, the oil price shocks have lower impact on interest rates, as reported in Panel B. Less than 2 percent of volatility in interest rates can be explained by the volatility in oil price shocks up to the 3 rd month. The impact of oil price shocks on interest rates is less than 3 percent in all horizons. As mentioned above, the interest rates have considerable impact on real stock returns. We find that the reverse is also true. The real stock returns are the source of more than 14 percent of volatility in interest rates. The sources of volatility in industrial production are reported in Panel C. The results suggest that none of the variables is the major source of the volatility in the industrial production. The main source of industrial production volatility is the industrial production itself. Although it seems unlikely for the macroeconomy of Turkey to have a significant impact on world oil prices, we also check if there is any impact of macroeconomic variables on the variance of oil prices. We do this because Turkey is a member of the Group of Twenty (G20); hence, Turkish economy may be large enough to have some impact on the world oil prices. The results, reported in Panel D, suggest that both real stock returns and industrial production explain 2 percent of forecast error variance of oil prices. Interest rates do not have any significant impact on the volatility of oil prices in the short run, while in the long run the impact is slightly higher than 1 percent. 3.2 Impulse Responses The generalized impulse responses of real stock returns, interest rates, industrial production and real oil prices are depicted in Figures 1 through 4, respectively. Oil price shocks and industrial production shocks do not seem to have significant initial impacts on the real stock returns in ISE (Figure 1.). In Greece (Papapetrou, 2001), the

The Empirical Economics Letters, 5(4): (July 2006) 214 initial impact of oil price shocks on stock returns seem to be significant. However, there appears to be a significant positive spike in returns over the second horizon in the case of an oil shock. Not surprisingly, the only significant initial impact belongs to the interest rate and is negative. This negative impact is also confirmed when the system is shocked by interest rates as depicted in Figure 2. After the initial negative impact, a positive and maximum response is reached in the third month. Figure 1: Generalized Responses to Real Stock Returns Response of Real Stock Returns 20 15 10 5 0-5 Response of Interest Rates.02.01 -.01 -.02 -.03-10 -.04 Response of Industrial Production.03.02.01 -.01 -.02 Response of Real Oil Prices.03.02.01 -.01 -.02 -.03 -.03

The Empirical Economics Letters, 5(4): (July 2006) 215 Figure 2: Generalized Responses to Interest Rates Response of Real Stock Returns 8 Response of Interest Rates.08 4 0-4.06.04.02-8 -.02 Response of Industrial Production.015.010 5 0-5 -.010 -.015 Response of Real Oil Prices.02.01 -.01 -.02 -.020 -.03

The Empirical Economics Letters, 5(4): (July 2006) 216 Figure 3: Generalized Responses to Industrial Production Response of Real Stock Returns 6 Response of Interest Rates.03 4 2 0-2 -4.02.01 -.01-6 -.02 Response of Industrial Production.08 Response of Real Oil Prices.02.01.04 -.01 -.04 -.02 -.03

The Empirical Economics Letters, 5(4): (July 2006) 217 Figure 4: Generalized Responses to Real Oil Prices Response of Real Stock Returns 8 6 4 2 0-2 -4 Response of Interest Rates.015.010 5 0-5 -.010 -.015-6 -.020 Response of Industrial Production.03 Response of Real Oil Prices.12.02.01 -.01.08.04 -.02 -.04 It seems that industrial production does not have a significant impact on the variables in the system except the interest rates. In the fifth month, the response of interest rates to innovation in industrial production is positive and significant, and in the 9 th month the response is negative. Although the initial impact of industrial production on oil prices is positive, the results are not significant. Figure 4 depicts the response of variables in the system to the shock to oil prices. The initial response of stock returns is negative, though not significant. In the second month response is positive and significant.

The Empirical Economics Letters, 5(4): (July 2006) 218 4. Conclusions In this study we investigate the importance of oil price shocks for the real returns in Turkish stocks traded in the Istanbul Stock Exchange market. We find that oil price shocks do not seem to affect the real stock returns significantly in Turkey. This finding appears to be different from the results obtained in other studies on OECD countries including a developing country, Greece. Our findings suggests that innovations in oil prices do not affect the price level in Turkey, indicating the need for more research on why oil price shocks do not seem to affect the macroeconomic variables in Turkey. One possible explanation may be that the Turkish government imposes a high excise tax on oil and the shocks in the world oil prices may be absorbed by the changes in the tax rate; hence, not fully reflecting oil price shocks directly on the stock returns or the price level. References Brown, S.P.A. and M.K. Yucel, 1999, Oil prices and U.S. aggregate economic activity: A question of neutrality, Working Paper, Federal Reserve Bank of Dallas. Burbidge, J. and A. Harrison, 1984, Testing for the effects of oil-price rises using vector autoregressions, International Economic Review, 25, 459-484. Cheung, Y. W., Yuen, J., 2002, Effects of U.S. Inflation on Hong Kong and Singapore, Journal of Comparative Economics, 30, 603-19 Dickey, D. A., and W. A Fuller, 1979, Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Society, 75, 427-431. Ferderer, J.P., 1996, Oil price volatility and the macroeconomy, Journal of Macroeconomics, 18, 1-26. Gisser, M. and T.H. Goodwin, 1986, Crude oil and the macroeconomy: Tests of some popular notions: Note, Journal of Money, Credit and Banking, 18, 95-103. Hamilton, J.D., 1983, Oil and the macroeconomy since World War II., The Journal of Political Economy, 91, 228-248. Hamilton, J. D., 1994, Time Series Analysis, Princeton University Press, Princeton.

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