Working Paper Revision

The Contribution of Jump Activity and Sign to Forecasting Stock Price Volatility

Abstract: This paper proposes a novel approach to decompose realized jump measures by type of activity (finite/infinite) and by sign. We also provide noise-robust versions of the ABD jump test (Andersen et al. 2007) and realized semivariance measures for use at high frequency sampling intervals. The volatility forecasting exercise involves the use of different types of jumps, forecast horizons, sampling frequencies, calendar and transaction time-based sampling schemes, as well as standard and noise-robust volatility measures. We find that infinite (finite) jumps improve the forecasts at shorter (longer) horizons; but the contribution of signed jumps is limited. Noise-robust estimators, that identify jumps in the presence of microstructure noise, deliver substantial forecast improvements at higher sampling frequencies. However, standard volatility measures at the 300-second frequency generate the smallest MSPEs. Since no single model dominates across sampling frequency and forecast horizon, we show that model averaged volatility forecasts - using time-varying weights and models from the model confidence set - generally outperform forecasts from both the benchmark and single best extended HAR model.

Keywords: Volatility Forecasts; Realized Volatility; Finite Activity Jumps; Infinite Activity Jumps; Signed Jumps; Noise-Robust Realized Volatility; Model Averaging;

JEL Classification: C22; C51; C53; C58;

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Bibliographic Information

Provider: Federal Reserve Bank of Dallas

Part of Series: Working Papers

Publication Date: 2020-11-17

Number: 1902

Note: A previous version of this paper circulated under the title "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility."

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