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

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  • Ruijun Bu

    ()

  • Rodrigo Hizmeri
  • Marwan Izzeldin
  • Anthony Murphy
  • Mike G. Tsionas

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.

Suggested Citation

  • Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2019. "The Contribution of Jump Activity and Sign to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Nov 2020.
  • Handle: RePEc:fip:feddwp:1902
    DOI: 10.24149/wp1902r1
    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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    More about this item

    Keywords

    Volatility Forecasts; Realized Volatility; Finite Activity Jumps; Infinite Activity Jumps; Signed Jumps; Noise-Robust Realized Volatility; Model Averaging;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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