Absolute return predicts volatility better based on the research of China Stock Market

Volatility forecasting is core to many risk management problems. We show that the empirical results complement the theoretical analysis suggested before. We start from a continuous time stochastic volatility model for asset returns suggested by Barndorff-Nielsen and Shephard (2001) and study the persistence and linear regression properties. We find that RAV is the most preferred regressor to predict future increments at different prediction horizons.