Optimal Threshold Selection for Realized Volatility Forecasts in the Presence of Jumps

Abstract

When estimating and forecasting realized volatility in the presence of jumps, a form of bias-variance tradeoff is present in the selection of the truncation threshold. We propose an optimal method for threshold selection that minimizes the out-of-sample forecasting loss. The use of a forecasting framework is fundamentally different from the testing framework in the literature. We find that a priori large truncation thresholds may not be optimal from a forecasting perspective and smaller thresholds should be used. An extensive simulation study and an empirical application to S&P 500 futures demonstrate the effectiveness of the proposed method.