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The last decade has seen substantial advances in the measurement, modelling and forecasting of volatility which has centered around the realized volatility literature. To date, most of the focus has been on the daily and monthly frequencies, with little attention on longer horizons such as the quarterly frequency. In finance applications, forecasts of volatility at horizons such as quarterly are of fundamental importance to asset pricing and risk management. In this article we evaluate models for stock return volatility forecasting at the quarterly frequency. We find that an autoregressive model with one lag of quarterly realized volatility with an in-sample estimation period of between 60 and 80 quarters produces the most accurate forecasts, and dominates other approaches, such as the recently proposed mixed-data sampling (MIDAS) approach.
