Is intraday data useful for forecasting VaR? The evidence from EUR/PLN exchange rate

Abstract

In this paper, we evaluate alternative volatility forecasting methods under Value at Risk (VaR) approach by calculating one-step-ahead forecasts of daily VaR for the EUR/PLN foreign exchange rate within the 4-year period. Using several risk models, including GARCH specifications and realized volatility models as well as hybrid of these two, we examine whether incorporation of intraday data allows to produce better one-step-ahead volatility forecasts in daily horizon than in case of using daily data only. The volatility forecasts are compared within VaR framework in two-step procedure: the statistical accuracy test are conducted as well as the loss functions are obtained. We find that GARCH models produce better backtesting results than models for realized volatility. When the loss functions of the models that passed the first-stage filtering procedure are compared, there is no distinct winner of the race. We also find no evidence that skewed Student t distribution assumption within GARCH models provides better VaR forecasts when compared to symmetric Student.