Toward better risk forecasts - Using data with higher frequency than the forecasting horizon.

Volatility forecasts crucial to many financial applications usually assume implicitly that the frequency of the data should match the forecast horizon; portfolio managers typically rely on risk models estimated using monthly data to produce monthly volatility forecasts, for example. For longer-term forecasts, this practice has two drawbacks: Volatility estimates can be based on stale data; and return events occurring within long sampling intervals are obscured, confounding estimation. Monthly volatility risk measures constructed using higher-frequency data seem to be more robust than those using low-frequency data. Microstructure effects can explain the differences in estimates.