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The Contribution of Jump Signs and Activity 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 (infinite/finite) and by sign. It also provides noise-robust versions of the ABD jump test (Andersen et al., 2007b) 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.

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  • Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, "undated". "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 202109, University of Liverpool, Department of Economics.
  • Handle: RePEc:liv:livedp:202109
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    1. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.

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    More about this item

    Keywords

    Realized volatility; Signed Jumps; Finite Jumps; Infinite Jumps; Volatility Forecasts; Noise-Robust 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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