Asymmetry and Long Memory in Volatility Modeling

In this paper, we propose a long memory asymmetric volatility model, which captures more flexible asymmetric patterns as compared with several existing models. We extend the new specification to realized volatility (RV) by taking account of measurement errors and use the Efficient Importance Sampling technique to estimate the model. We apply the model to the RV of S&P500. Overall, the results of the out-of-sample forecasts show the adequacy of the new asymmetric and long memory volatility model for the period including the global financial crisis.