Siab Mamipour; Ziba Sasanian Asl
Abstract
Financial markets are sensitive to exchange rate fluctuations of the Iran’s economy. Changes in the foreign exchange market affect household, businesses, and government spendings. Exchange rate management policy helps stock market to be protected from the effects of exchange rates. As for investment ...
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Financial markets are sensitive to exchange rate fluctuations of the Iran’s economy. Changes in the foreign exchange market affect household, businesses, and government spendings. Exchange rate management policy helps stock market to be protected from the effects of exchange rates. As for investment strategies, investors can invest without considering the exchange rate in the short run investments, but exposure to asymmetric exchange rate is very important in long run.
This study explores the asymmetric exchange rate exposure of stock returns building upon the capital asset pricing model (CAPM) framework, using monthly returns of Iranian industry indices. In accordance with the existing literature, industry returns are subject to lagged exposure effects, but the asymmetries vary across industries, which could be due to the discrepancies of trade balance and ownership of certain industries.
Furthermore, the dynamic multipliers depict that industry returns quickly respond to changes in the exchange rate and correct the disequilibrium within a short time, making the long run exposure to be symmetric or very small (Cuestas & Tang, 2015).
Methodology
The main aim of this study is, hence, to investigate the asymmetric exchange rate exposure of stock returns in the Iranian stock market at the industry level. Specifically, we introduce the conventional CAPM for measuring exchange rate exposure. We construct the dynamic nonlinear model to investigate both the long run and short run asymmetric exposure effects, which is carried out by means of estimating a nonlinear autoregressive distributed lag (NARDL) model introduced by Shin and Greenwood-Nimmo (2014).
Building upon the CAPM structure, this paper contributes to a growing literature on the analysis of exchange rate exposure of Iran's stock market on the following grounds. First, compared to linear regression models, the NARDL model demonstrates its competence and efficiency in estimating the exchange rate exposure. The disparities in the exposure effect depend on the ownership of these companies and the expansion of their global operations. Second, industry returns strongly and quickly respond to exchange rate changes in the very short run, while most of the long run exposures are symmetric or very small.
In fact, this paper studies the effects of positive and negative shocks of exchange rate on the return of various industries in stock market based on CAPM model and NARDL approach to estimate parameters during the period of April 2012 to March 2015.
To evaluate the efficiency of asymmetric effects of exchange rate on active industries in Tehran stock market, first exchange rates decompose to positive and negative shocks and then its asymmetric effects on stock market is analyzed using NARDL model. To do it, we use the Wald test for the symmetry or asymmetry effects of positive and negative exchange rate on return of active industries in the short run and long run.
Results and Discussion
The results indicate that most industries of stock market are under the influence of positive and negative shocks of exchange rate and these effects are different for industries. Hence, the effects of positive and negative exchange rate shocks for the industries such as agriculture, textile, rubber, engineering, leather, communication, steel products, radio, chemical materials, and multi-disciplinary industries are symmetric while the effect of exchange rate shocks on return of industries like bank, automobile, basic metals, publishing and printing, electrical devices, computer , tool medical, cement, finance, non finance, investments, paper, non-metallic minerals, and machinery industries are asymmetric in short run, and for industries of ceramic tiles, they are asymmetric in the long run. Additionally, in industries like mass production, oil, transport, coal, drugs, wood, sugar, food ingredients except sugar, they are asymmetric in the short run and long run.
Thus, the results of this study can be useful for investors and shareholders in predicting the short and long term effects of exchange rate shocks on the stock prices.
Therefore, it can be argued that sudden shocks exchange rate can affect about 70 percent of returns of active industries in Tehran Stock market. Therefore, avoiding sudden shocks and maintaining relative stability in exchange market are the main suggestions for policymakers. Also, given that the exchange rate shocks are exogenous variables for firm managers, investors should further evaluate the performance of companies and their profitability, and consider long run vision in analysis and making decisions.
Siab Mamipour; Atefeh Feli
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 ...
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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.
