Abstract
Previous research concerned with the investigation of intraday data has typically sought to model that data using techniques to control for intraday periodicity, has applied models of short-horizon and long-horizon dependencies, or has utilised intraday data in the construction of realised variance. Using Euro exchange rate data, we apply these different modelling strategies in forecasting daily volatility and calculating Value-at-Risk measures, benchmarked against a standard GARCH model for daily and raw intraday returns. Our results suggest that the use of intraday data provides improved daily volatility and VaR forecasts relative to daily data and daily realised volatility. Further, use of the raw intraday data, or intraday data subjected to a simple standardisation procedure, provides better forecasts and VaR measures than more complicated models for intraday periodicity. These results also hold in a multi-asset portfolio setting.
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