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Volatility forecasting using stochastic conditional range model with leverage effect

Author

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  • Xinyu Wu
  • Haibin Xie

Abstract

In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.

Suggested Citation

  • Xinyu Wu & Haibin Xie, 2019. "Volatility forecasting using stochastic conditional range model with leverage effect," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(5), pages 1156-1170, September.
    • Handle: RePEc:wly:apsmbi:v:35:y:2019:i:5:p:1156-1170
    DOI: 10.1002/asmb.2457
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    References listed on IDEAS

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    1. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    2. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1, January.
    3. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
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    Cited by:

    1. Khoo, Zhi De & Ng, Kok Haur & Koh, You Beng & Ng, Kooi Huat, 2025. "Forecasting financial volatility: An approach based on Parkinson volatility measure with long memory stochastic range model," Journal of Empirical Finance, Elsevier, vol. 82(C).
    2. Wu, Xinyu & Wang, Xiaona, 2020. "Forecasting volatility using realized stochastic volatility model with time-varying leverage effect," Finance Research Letters, Elsevier, vol. 34(C).

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