Computing value at risk with high frequency data

Current studies on financial market risk measures usually use daily returns based on GARCH type models. This paper models realized range using intraday high frequency data based on CARR framework and apply it to VaR forecasting. Kupiec LR test and dynamic quantile test are used to compare the performance of VaR forecasting of realized range model with another intraday realized volatility model and daily GARCH type models

We compare the computation of value at risk with daily and with high frequency data for the Deutsche mark-US dollar exchange rate. Among the main points considered in the paper are: (a) the comparison of measures of value at risk on the basis of multi-step volatility forecasts; (b) the computation of the degree of fractional differencing for high frequency data in the context of a Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) model; and (c) the comparison between deterministic and stochastic models for the filtering of high frequency returns.