Forecasting Daily Stock Volatility Using GARCH-CJ Type Models with Continuous and Jump Variation

In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path variation and discontinuous jump variation to provide a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. By using 5-minute high-frequency data of MASI Index in Morocco for the period (January 15, 2010 - January 29, 2016), we estimate parameters of the constructed GARCH and EGARCH-type models (namely, GARCH, GARCH-RV, GARCH-CJ, EGARCH, EGARCH-RV, and EGARCH-CJ) and evaluate their predictive power to forecast future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility while the discontinuous jump variation contains relatively less information for forecasting volatility. More interestingly, the findings show that the GARCH-CJ-type models have stronger predictive power for future volatility than the other two types of models. These results have a major contribution in financial practices such as financial derivatives pricing, capital asset pricing, and risk measures.