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Forecasting volatility using realized stochastic volatility model with time-varying leverage effect

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  • Wu, Xinyu
  • Wang, Xiaona

Abstract

This paper proposes a realized stochastic volatility model with time-varying leverage effect (hereafter the RSV-TVL model), in which the time-varying leverage effect is modelled based on a linear spline. The model parameters are estimated by using the maximum likelihood method based on a continuous particle filter. Simulation results show that the proposed estimation method works well. An empirical application to S&P 500 index highlights the value of incorporating the realized volatility measure and the time-varying leverage effect into volatility forecasting, and shows that the RSV-TVL model produces more accurate out-of-sample forecasts of volatility than the alternatives.

Suggested Citation

  • Wu, Xinyu & Wang, Xiaona, 2020. "Forecasting volatility using realized stochastic volatility model with time-varying leverage effect," Finance Research Letters, Elsevier, vol. 34(C).
    • Handle: RePEc:eee:finlet:v:34:y:2020:i:c:s1544612319305021
    DOI: 10.1016/j.frl.2019.08.019
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    More about this item

    Keywords

    Realized volatility measure; Stochastic volatility; Time-varying leverage effect; Linear spline; Continuous particle filter;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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