Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market

The properties of clustering, long memory, switching regime, leverage effect and heavy tail in volatility dynamic behavior are induced by important stylized facts in financial markets. There is still a controversy whether incorporating these properties could improve the modelling and forecasting performance of volatility. We construct hybrid volatility models via three perspectives including short memory, long memory and Markov switching GARCH with leverage effect and heavy tail, and empirically compare their performance of in-sample estimation, out-of-sample forecast and risk measurement based on trading data of Chinese equity market index. The outof-sample forecast results indicate that the FIEGARCH model with innovation distribution of GED outperforms the competing models, and the backtesting results of VaR and ES confirm that this model performs well in the application of risk measurement.