Evaluation of RGARCH Model to Estimate the Conditional Variance of Tehran Stock Exchange Index

Forecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization and risk management are examples for implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Realized GARCH (RGARCH) that considers simultaneous model for both realized volatility and conditional variance at the same time. In this article, we estimate conditional variance with GARCH, EGARCH, GIR-GARCH and RGARCH with two realized volatility estimators using Tehran Exchange Price Index (TEPIX). We compared models, for in sample fitting, by the log likelihood value and used MSE and QLIKE lose functions to evaluate predicting accuracy. The results show that the RGARCH method for TEPIX outperforms the other methods in both ways. So, using RGARCH model in practical situations like pricing and risk management would tend to better results.