Forecasting Stock Market Realized Volatility using decomposition
Empirical studies concerned with realized volatility reveal the presence of heterogeneous behavior within the stock market. The sum of this heterogeneous behavior takes a persistent form, which may be modeled and forecasted according to different time horizons by the class of HAR models. In this paper, we investigate the HAR-RV and HAR-RV-CJ models for high-frequency data based on five realized volatility indices. The aim here is to demonstrate that the predictability of realized volatility can be improved by decomposing realized variance into its continuous and jump components. What is more, the results show that this decomposition of the realized variance into its components does indeed enhance the modeling and forecasting of the indices’ realized volatility.
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