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Bayesian estimation of the stochastic volatility model with double exponential jumps

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  • Jinzhi Li

    (Minzu University of China)

Abstract

This paper generalizes the stochastic volatility model to allow for the double exponential jumps. To derive the jumps and time-varying volatility in returns, we implement an efficient Markov chain Monte Carlo approach based on the band and sparse matrix algorithms used in Chan and Hsiao (SSRN Electron J., 2013, https://doi.org/10.2139/ssrn.2359838 ) to estimate this model. We illustrate the the methodology using the daily data for the Shanghai Composite Index, Hangseng Index, Nikkei 225 Index and Kospi Index. We find that the stochastic volatility model with double exponential jumps provide better fitness in sample period.

Suggested Citation

  • Jinzhi Li, 2021. "Bayesian estimation of the stochastic volatility model with double exponential jumps," Review of Derivatives Research, Springer, vol. 24(2), pages 157-172, July.
    • Handle: RePEc:kap:revdev:v:24:y:2021:i:2:d:10.1007_s11147-020-09173-1
    DOI: 10.1007/s11147-020-09173-1
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    References listed on IDEAS

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    More about this item

    Keywords

    Stochastic volatility; Double exponential jumps; MCMC; Stock indexes;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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