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

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.