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

IntroductionThe 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.
MethodologyIn 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 DiscussionResults 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 SuggestionsNumerous 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.