world markets, a variety of volatility can be observed in stock market, which can exert different effects on the economy of a country and results in developing suitable economic policies. In the present article, the stock market volatility was first modelled using the data of Tehran Stock Exchange from June 1992 to January 2012, then Markov-switching model was used to predict stock market volatility in a non-eventual state. The reason for using the model is that there is a possibility of switching or transition between two regimes for the indexes of the model. As a result of this, normal distribution and t distribution as well as GED were observed for errors. In order to predict stock market volatility of Iran, the performance of the Markov-switching model can be way better than other distributions with respect to t error distribution with degrees of freedom between the two regimes. According to transition probabilities, volatility of stock price would hinder the improvement of the situation, as it may cause stock market switch from a situation to a lower one. However, the great tendency for stability of the situation of a regime in proportion to transition to another situation indicate that an accurate planning can hinder the transition to lower situation (recession) K
کلید واژگان :EYWORDS Markov-switching , transition probability , volatility , regime
ارزش ریالی : 300000 ریال
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