Abstract
This paper compares model-based and reduced-form forecasts of financial volatility when high-frequency return data are available. We derived exact formulas for the forecast errors and analyzed the contribution of the wrong data modeling and errors in forecast inputs. The comparison is made for feasible forecasts, i.e. we assumed that the true data generating process, latent states and parameters are unknown. As an illustration, the same comparison is carried out empirically for spot 5-minute returns of DM/USD exchange rates.
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