Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?

Abstract

Abstract Estimation windows, either rolling or expanding, are used for volatility forecasting. In this study, we propose a new approach relying on both estimation windows. Our method is based on how well these two windows performed in terms of prediction during a recent period of past time. We will continue to use whichever one has performed better in the past. Results show that our strategy significantly outperforms the individual and mean combination models. Whether the window is rolling or expanding, the relatively better performance is persistent. In other words, we document the existence of the momentum of predictability (MoP). A mean–variance investor can achieve the highest utility gains using our strategy for volatility forecasting. Moreover, the results pass a series of robustness tests.

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