The Impact of Oil Price Volatility on Tehran Stock Market at Sector-Level: A Variance Decomposition Approach

اقتصاد پولی مالی

Article 9, Volume 24, Issue 13, April 2017, Pages 205-236   

Document Type: پژوهشی

DOI: 10.22067/pm.v24i14.58846

Authors

Siab Mamipour^* ; Atefeh Feli

Kharazmi

Abstract

Introduction
The empirical evidence has shown that markets are not isolated from each other and volatilities of markets are associated with each other. Stock Exchange market is a market to trade stocks based on specific rules and regulations. Many factors affect shaping the information and the views of market parties and the stock price. Some of these factors are internal and some are external factors with regards to state of the domestic economy. In the meantime, fluctuations in world oil price, as a powerful exogenous variable, can affect many macroeconomic variables espacilly stock prices indices in Iran. The oil market is one of the most important markets which affect financial markets in Iran as a country that has been relied on oil resurce. In order to make appropriate decisions in making a portfolio, investors should be aware of the relationships between markets.
Methodology
In this regard, this study investigates spillover effects of oil price volatility on stock return of selected (37) industries in the Tehran Stock Exchange during December 2008 to March 2016 with weekly frequency. We used Markov switching model to identify and decompose sataes of oil price with different regims and then the spillover effect of oil price on stock market is analyzed using forecast-error variance decomposition method introduced by Diebold and Yilmaz (2012) in the framework of a Generalized VAR. Thus, first, oil price decompose to different sataes like high and low volatility by using a Markov switching model and OX Metrix software. Then, we studied spillover effects of volatilities in the oil market with different states on the stock market using RATS software.
Results and Discussion
Results of the unit root test (ADF, PP & KPSS) implies that all variables reject the existence unit root and all variables are stationary at level, then, LR test is used to be sure of the non-linearity relation of the variables. The LR test shows that using non-linear model is suitable. To do Markov switching model, we must select the optimal model between different switching paprameters and diffrernt lags. Finally, MSIH(2, 3) is selected as the optimal model by minimizing Akaike information criterion. The MSIH(2, 3) model is an auto-regressive model that has two regimes and three autoregressive coefficient, and variance and intercept are regime switching dependent. The estimated coefficients of the model are statistically significant. Since there is a very high probability of transition from regime 1 to itself, hence, regime 1 is the most stable regime. Since transition probability from each regime to itself is very high and is about 96% percent, on the other hand, if the market in period t is in regime zero (or one), we expect it will stay at the same regime in the period t + 1 with 96% probability and shift to the other regime in period t + 1 with %4 probability. The average durability in both regimes lasts almost 31 weeks. This means that every time that oil price is in the regime zero (or one) it is expected to stay by 31 weeks in this regime.
The results of variance decomposition model (generalized VAR) shows that more than 90% of the forecast-error variance of both markets (oil and stock) are the low volatility regime (regime 0).
The results show the volatility spillover effects of the oil price on the stock market in the low volatility regime (regime zero) is less than the high volatility regime (regime 1) and volatility spillover in the high volatility regime is more extensive. The transmission of oil shocks in regim 1 is high compared to regime 0. The paper results also show that the highest amount of volatility spillover of the oil market relates to index of "basic metals industry"; "Chemical", "Publish and print", "Cement", "Non-metallic minerals", "Communications equipment" and "Rubber" industry in regime 0, and "Metal minerals", "Engineering", "Paper products", "Petroleum products", "Other mines" and "Extraction" industries in the regime1 are next levels.
Conclusions and Suggestions
Numerous studies have been done on the possible relationship between domestic financial markets and international variables like oil price, but most of the studies have used methods such as multivariate GARCH models that can only answer the questions like if there is volatility between the markets or not. A similar approach can be used to evaluate and quantify spillovers between different indicators of stock markets and commodity markets. It is also possible to study the causative factors of spillovers among different markets, to be able to fully manage and forecast them. Investors should consider the relationship and spillover between financial markets and how the stock market indices are affected by the oil volatilities in the portfolio selection. With regard to industries that are less affected by the spillover shocks, they can reduce their investment risk. They can use the results of this research in their stock portfolio.

Keywords

Volatility Spillover; Stock Market; oil return; Markov Switching; variance decomposition method

References

[1] Borhani Fard, M. J., & Mehrabian, A. (2013). Effect of volatilities in the global price of crude oil on the stock price index of petrochemical companies: The first electronic national conference:Iran's economic outlook (support of national production approach). Isfahan. (In Persian)

[2] Dastgir, M., & Zafari, F. (2009). The role of accounting information in predicting stock returns index. mahname bourse, 85, 48-55. (In Persian)

[3] Jahangiri, kh., & Hekmati Farid, S. (2014). Effects of volatilities spillover of stocks, gold, oil markets and currencies. Journal of Economic Research, 15(56), 161-194. (In Persian)

[4] Khiabani, N., & Dehghani, M. (2014). The role of oil market in volatility of Dollar, Euro & gold markets. Journal of Economic Research, 19(58), 207-238. (In Persian)

[5] Khodaveisi, H., Mansourfar, GH., & Azarnioush, F. (2013). The impact of crude oil price volatilities on the stock index of selected active industries in the Tehran Stock Exchange. (MA Thesis), Urmia university. (In Persian)

[6] Sadeghi, M., & Mohseni, H. (2013). The impact of oil prices on stock market returns: Evidence from oil exporting countries in the Middle East. Research energy policy and planning, 1(3), 1-16. (In Persian)

[7] Seyed Hosseini, S. M., & Ebrahimi, S. B. (2013). Modelling and measuring the volatilities spillover using multivariate GARCH models Case study: Iran, UAE and global oil prices. Journal of Securities Exchange, 21, 137-157. (In Persian)

[8] Aloui, C., & Jammazi, R. (2009). The effects of crude oil shocks on stock market shifts behavior: A Regime Switching Approach. Energy Economics, 31(5), 789-799.

[9] Arouri, M. H., Lahiani, A., & Nguyen, D. Kh. (2011). Return and volatility transmission between world oil prices and stock markets of the GCC countries. Economic Modelling, 28(4), 1815-1825.

[10] Arouri, M. H., & Rault, Ch. (2010). Causal relationships between oil and stock prices: some new evidence from gulf oil-exporting countries. International Economics, 122, 41–56.

[11] Awartani, B., & Maghyereh, A. I. (2013). Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries. Energy, 36, 28–42.

[12] Backus, D. K., & Crucini, M. J. (2000). Oil Prices and Terms of Trade. Journal of International Economics, 50(1), 185–213.

[13] Branger, N., Kraft, H., & Meinerding, C. (2009). What is the Impact of Stock Market Contagion on an Investor's Portfolio Choice? Insurance: Mathematics and Economics, 45(1), 94-112.

[14] Chang, Ch. L., McAleer, M., & Tansuchat, R. (2013). Conditional correlations and volatility spillovers between crude oil and stock index returns. The North American Journal of Economics and Finance, 25, 116–138.

[15] Chen, S. S. (2009). Do higher oil prices push the stock market into bear territory? Energy Economics, 32(2), 490− 495.

[16] Diebold, F. X., & Yilmaz, K. (2012, March). Better to Give than to Receive:Predictive Directional Measurementof Volatility Spillovers Forthcoming. International Journal of Forecasting, 28(1), 57-66.

[17] Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.

[18] Ewing, b. T., & Malik, f. (2010). Estimating Volatility Persistence in Oil Prices under Structural Breaks. Financial Review, 215(4), 1011-1023.

[19] Filis, G., Stavros, D., & Christos, F. (2011). Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152–164.

[20] Forbes, K., & Rigobon, R. (2000). Contagion in Latin America: Definitions,Measurement, and Policy Implications. NBER Working Paper, 1, 1-46.

[21] Gray, S. (1996). Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process. Journal of Financial Economics, 42(1), 27-62.

[22] Hamilton, J. (1989). A new approach to the economic analysis of non-stationary time series and the business cycle. Econometrica, 57(2), 357–384.

[23] Jones, C., & Kaul, G. (1996). Oil and stock markets. Journal of Finance, 51, 463 - 491.

[24] Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147.

[25] Krolzig, H. (1997). Markov-Switching Vector Autoregressions, Modelling, Statistical Inference and Applications to Business Cycle Analysis. Berlin: Springer.

[26] Lardic, S., & Mignon, V. (2006). The impact of oil prices on GDP in European countries: An empirical investigation based on asymmetric cointegration. Energy Policy, 34(18), 3910–3915.

[27] Malik, F., & Hammoudeh, S. (2007). Shock and volatility transmission in the oil, US and Gulf equity markets. International Review of Economics and Finance, 16, pp. 357-368.

[28] Mensi, B., & Boubaker, M. (2013). Correlations and Volatility Spillovers Across Commodity and Stock Markets: Linking Energies, Food and Gold. Economic Modelling, 32, 15-22.

[29] Miller, J. I., & Ratti, R. A. (2009). Crude oil and stock markets: stability, instability, and bubbles. Energy Economics, 31(4), 559−568.

[30] O'Neill, J. T., Penm, J., & Terrell, D. R. (2008). The role of higher oil prices: a case of major developed countries. Research in Finance, 24, 287−299.

[31] Park, J., & Ratti, R. A. (2008). Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics, 30(5), 2587–2608.

[32] Pesaran, M. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29.

[33] Sadorsky, P. (1999). Oil price shocks and stock market activity. Economics, Energy, 21(5), 449–469.

[34] Serra, T. (2011). Volatility spillovers between food and energy markets: A semi parametric approach. Energy Economics, 33(6), 1155–1164.

[35] Wu, H., & Li, Sh. (2013). Volatility spillovers in China’s crude oil, corn and fuel ethanol markets. Energy Policy, pp. 878–886.

[36] Zhang, B., & Wang, P. (2014). Return and volatility spillovers between china and world oil markets. Economic Modelling, pp. 413–420.

[37] Zhang, Y., & Jun, S. (2016). The dynamic volatility spillover between European carbon trading market and fossil energy market. Journal of Cleaner Production, 112(4), 2654–2663.