Asymmetry dynamic volatility forecast evaluations using interday and intraday data

Abstract

The accuracy of financial time series forecasts often relies on the model precision and also the availability of actual observations for forecast evaluations. This study aims to tackle these issues in order to obtain a suitable asymmetric time-varying volatility model that outperformed the forecast evaluations based on interday and intraday data. First, the model precision is examined based on the most appropriate time-varying volatility representation under the autoregressive conditional heteroscedascity framework. Second, the forecast evaluations are conducted under three loss functions using the volatility proxies and realized volatility. Finally, the empirical studies are implemented on two major financial markets and the estimated results are applied in quantifying their market risks.