A Generalized Discrete-Time Long Memory Volatility Model for Financial Stock Exchange

Abstract

We proposed a simple way to combine a few long memory models in financial market volatility modeling using daily, range and high frequency data. This model was able to fit the return, range of daily return or realized volatility under a parametric heavy-tailed distribution. Model was flexible to include additional volatility information as the contemporaneous variables. Empirical results found that the proposed model provides substantial improvement in the model fitting, specification and most importantly, a better out-of sample forecasting in the Malaysian stock market.