Forecasting value-at-risk and expected shortfall using high frequency data modeling

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk and expected shortfall across 3 industry indices in Tehran Stock Exchange such as chemical, vehicle and metals. The dataset is composed of daily data covering the period from May, 2011 to May, 2015. According to the result of this research accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-day-ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered in 1-day, 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons.