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Direct volatility modeling
Volatility forecasts are often required across a range of horizons to manage risk. This paper studies the forecast performance over horizons out to one month. Particular attention is paid to the choice between iterating a daily model and estimating a horizon-specific model. Forecasts from the latter are often referred to direct forecasts. Direct forecasts may be preferable if the model used to produce iterative forecasts is meaningfully misspecified. Both forecasting methods are compared using a panel of 25 financial asset return series covering the major assets classes. Iterative models are found to out-perform direct forecasting methods across a wide range of horizons and assets. Direct forecasts are only found to perform better than iterative forecasts when for a small subset of models when the estimation window is long. Extensions to asymmetric models show that adding conditional asymmetries improves out-of-sample performance although the ranking between iterative and direct forecast is unaltered.
