مسلم پیمانی

The relationship between risk and return is not symmetric under different circumstances. As the prospect theory describes, the value function which passes through the reference point is steeper for losses than gains (asymmetric risk appetite). But such an asymmetrical risk aversion could be traced in different periods of investment and market boom and bust cycles behind the reference point. Moreover, investors’ asymmetric behavior is different regarding various risks, such as market risk, illiquidity risk, and credit risk. This paper examines the asymmetric investors' reaction to various risks in Tehran Stock Exchange (TSE) both in recession and growth from 2011 through 2016. Evidence reveals that although all three kinds of risks are relevant, especially illiquidity risk, risk factors’ explanation power in the bullish market is less than the bearish one. This indicates that investors tend to show an asymmetric reaction to risk in up and downswing markets. The asymmetric behavior is also predominant due to investors’ weak attention to the market risk in a growing market in opposition to a recessive market condition that turns out to be an important risk consideration. The results of this study can help investors to consider asymmetrical behavior effect when they are making their minds on investment decisions.

Volatility and risk measurement are essential parameters in risk management programs that can affect economic activities and public confidence in the stock market. Also, these two are the keys in the studies that connect the stock market, economic growth, and other financial factors. In recent years, due to the instability in the Tehran Stock Exchange, controlling the adverse effects caused by the volatility of stock prices, predicting and modeling price dynamics, and measuring risk have become necessary for the participants in this market. In the present research, the class of hidden Markovian index models of conditional variance Heteroskedasticity (HM-GARCH) is used to predict the volatility of stock prices and accounts of the Tehran Stock Exchange. For a comprehensive review, the models are selected to include the characteristics of volatility clustering, asymmetry in volatility (leverage effect), and heavy tail of stock returns (with t-student distribution). Based on RMSE and AME criteria, the HM-EGARCH-Normal Exponential GARCH model with normal distribution is more effective than other models in predicting stock market volatility. Therefore, leverage is necessary to analyze stock market risks using hidden Markov models, but heavy tail distribution is unnecessary. The results indicate that the HM-EGARCH-Normal model appropriately assesses volatility and improves market transparency and risk management forecasts. Also, the VaR and CVaR market risk assessment post-tests using Kupiec and DQ tests do not show evidence of overestimation or underestimation.