The Impact of World Commodity Price Index on Tehran Stock Exchange Returns: The Bayesian Approach of Markov Switching Method

Document Type : Research Paper

Authors

1 Assistant Prof., Department of Economics, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.

2 Assistant Prof., Department of Management, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran.

Abstract

Objective: The commodities especially oil, wheat and iron have key role in economy because of they are the main components of many common goods in human lives. An increases or a decrease in the commodity prices affects the economies all over the world. Despite the importance of commodity prices, only few studies have emphasized their impact on stock prices. This study contributes to the empirical literature about the relationships between stock and commodity markets. Given that the Tehran Stock Exchange is a commodity-based market, the purpose of this study is to investigate the effect of world commodity price on Tehran stock returns.
Methods: In this study, the monthly data of stock market during the period of 2009–2019 were used by applying Markov switching model with time-varying transition probabilities (MS-TVTP).
Results: Based on the results, model MSIH (2)-AR (1) has been chosen as the optimal model. In the estimated model, the first regime determines the lower stock return and the second regime determines the higher stock return, and transmission probabilities in two models represent the persistence of first regime in the stockmarket of Iran. In addition, results show that one percent increase in commodity price will lead to 0.343 percent increase in stock return, but in the higher Stock return regime, lead to a 1.133 percent increase in stock returns. In this regard, the inequality of the two coefficients in the two regimes has confirmed by the Wald test. Also, expected duration in lower stock return regime is about 12 months and in higherstock regime is about 6 months.
Conclusion: This study illustrates the asymmetric effect of commodity priceon stockreturn in various regimes in Iran. It indicates that lower stock return regime is more stationary. Therefore, this study proposes to use the commodity price index as a warning indicator of a change in the stock return regime for investors.

Keywords


Abiad, A. (2003). Early-Warning System: A Survey and a Regime-Switching Approach. IMF Working Paper WP/03/32.
Amir Teimoori, R., Jalaee, S. A. & Zayandeh Roodi, M. (2017). Investigating the Impact of Iran-Germany Business Cycle Synchronization on the Friction and Depth of Financial Markets in Iran (Markov Switching Bayesian VAR Method). Journal of Financial Research, 19 (3), 341- 364. (in Persian)
Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking and Finance, 34, 1886-1898.
Bilal, A. R., &Talib, N. A. (2013). How Gold Prices Correspond to Stock Index: A Comparative Analysis of Karachi Stock Exchange and Bombay Stock Exchange. World Applied Sciences Journal, 21(4), 485-491.
Creti, A., Joets, M., & Mignon, V. (2013). On the links between stock and commodity markets' volatility. Energy Economics, 37, 16-28.
De Boyrie, M. E. & Pavlova, I. (2018). Equities and Commodities Co-movements: Evidence from Emerging Markets, Global Economy Journal, 18(3), 1-14.
Farooki, M. Z., & Kaplinsky, R. (2011). The Impact of China on Global Commodities: The Disruption of the World’s Resource Sector. London: Routledge.
Filardo, A. J. (1994). Business Cycle Phases and Their Transitional Dynamics. Journal of Business and Economic Statistics, 12(3), 299-308.
Filardo, A.J., & Gorgon, S. F. (1998). Business Cycle durations. Journal of Economic, 85(1), 99-123.
Ghaderi, S., & Rostami Noroozabad, M. (2017). Financial Globalization and Stock Return: Theory and Evidence from Time Series Data. Journal of Financial Research, 18(4), 715-734. (in Persian)
Ghaderi, S., Rostami Noroozabad, M. (2016). Financial Globalization and Stock Return: Theory and Evidence from Time Series Data. Journal of Financial Research, 18(4), 715-734. 
(in Persian)
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Non-stationary Time Series and the Business Cycle. Econometrica, 57, 357–384.
Hamilton, J. D., & Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of econometrics, 64(1-2), 307-333.
Ildırar, M., Iscan, E. (2016). The Interaction between Stock Prices and Commodity Prices: Eastern Europe and Central Asia Case. International Journal of Economics and Finance Studies, 8 (2), 94-106. 
Jahangiri, K., & Hosseini Ebrahim, S.A. (2017). The effects of monetary policy, exchange rate and gold on the stock market in Iran using MS-VAR-EGARCH model. Journal of Financial Research, 19 (3), 389- 414. (in Persian)
Jena, P.K., Goyari, P. (2016). Empirical Relationship between Commodity, Stock and Bond Prices in India: A DCC Model Analysis. The IUP Journal of Applied Finance, 22(1), 37-49.
Jin, H., & Jin, L. (2010). The impact of international oil prices on Chinese stock market - an analysis based on industry data. Financial Research, 2, 173-187.
Johnson, R., & Soenen, L. (2009). Commodity Prices and Stock Market Behavior in South American Countries in the Short Run. Emerging Markets Finance and Trade, 45(4), 69–82.
Kang, W., Ratti, R. A. & Vespignani, J. (2017). Global commodity prices and global stock volatility shocks: effects across countries, Working Papers 2017-05. University of Tasmania, Tasmanian School of Business and Economics.
Keong, M.K. (2014). Relationship between commodities market and stock markets: Evidence from Malaysia and China. (Doctoral dissertation, UTAR).
Kia, A. (2003). Forward-looking agents and macroeconomic determinants of the equity price in a small open economy, Applied Financial Economics, 13(1), 37–54.
Kilian, L. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review, 99(3), 1053-1069.
Kim, C. J., and Nelson, C. R. (1998). Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching, Review of Economics and Statistics, 80(2), 188-201.
Kuan, C. M. (2002), Lecture on the Markov Switching Model. Working Paper. Institute of Economics, Academia Sinica.
Lombardi, M. J. and Ravazzolo, F., (2016). On the correlation between commodity and equity returns: Implications for portfolio allocation, Journal of Commodity Markets, Elsevier, 2(1), 45-57.
Naifar, N. & Dohaiman, M. S. (2013). Nonlinear Analysis among Crude Oil Prices, Stock Markets' Return and Macroeconomic Variables. International Review of Economics & Finance, 27, 416-431.
Ntantamis, C., & Zhou, J. (2015). Bull and bear markets in commodity prices and commodity stocks: Is there a relation? Resource Policy, 43, 61-81.
Olson, E., Vivian, A.J., & Wohar, M. E. (2014). The relationship between energy and equity markets: Evidence from volatility impulse response functions. Energy Economics, 43, 297-305.
Qiao, Y. (2014). Dynamic Correlation between Selected World Major Stock Markets and Commodity Markets. Master’s Thesis. University of Northern British Columbia.
Raee, R., Mohmadi, SH., Saranj, A. (2014). Tehran Stock Exchange dynamics in a Markov regime switching EGARCH-in-mean model. Journal of Financial Research, 16(1), 77-98. (in Persian)
Sajjad, R, & Taherifar R, (2016). Confidence interval Calculation & Evaluating Markov regime switching Precision for Value-at-Risk Estimation: A Case Study on Tehran Stock Exchange Index (TEDPIX). Journal of Financial Research, 18(3), 461-482. (in Persian)