زهرا میلا علمی

This paper evaluates several GARCH models regarding their ability to forecast volatility n Tehran Stock Exchange (TSE). These include GARCH models with both Gaussian and fat­tailed residual conditional distribution, concerning their ability to describe and forecast volatility from 1­day to 22­day horizon. Results indicate that AR(2)­ MRSGARCH­GED model outperforms other models at one­day horizon. Also, the AR(2)­ MRSGARCH­GED as well as AR(2)­MRSGARCH­t models outperform other models at 5­day horizon. In 10 day horizon, three models of AR(2)­MRSGARCH outperform other models. Concerning 22 day forecast horizon, results indicate no differences between MRSGARCH models with that of standard GARCH models. Regarding Risk management out­of­sample evaluation (95% VaR), a few models seem to provide reasonable and accurate VaR estimates at 1­day horizon, with a coverage rate close to the nominal level. According to the risk management loss functions, there is not a uniformly most accurate model.