Comparing the Forecasting Ability of Deferent Models of Volatility in Tehran Exchange Dividend Price Index - Reza Tehrani; Mohammad Reza Pourebrahimi (فصلنامه پژوهش‌های اقتصادی ایران, 2009)
The present research, analyzses the forecasting performance of a variety of conditional and non-conditional models of TEDPIX volatility at the daily frequencies under three performance criteria:  namely Tthe root mean square error (RMSE), the mean absolute error (MAE) and the Theil  index. Under RMSE and Theil criteria, results show MA250, exponential smoothing,  and CGARCH models haved better performance among between non conditional and conditional models respectively.  Comparing forecasting performance of conditional and non conditional models shows that MA250 and ES models had better performance relative to conditional models. Other results of the study also reveal that according to conditional volatility models (except PARCH) there is a significant relationship between behavior of volatility and the targeted volatility range This result cannot be approved by ARMA. change of the price control whereas ARMA model rejects it. Furthermore, change of the time period of return measurement (daily and monthly) affects behavior of volatilitvolatility varies in different return.