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Multivariate Stochastic Volatility Model with Block Correlations

Author

Listed:
  • Han Chen

    (College of Finance and Statistics, Hunan University)

  • Yijie Fei

    (College of Finance and Statistics, Hunan University)

  • Jun Yu

    (Faculty of Business Administration, University of Macau)

Abstract

Modeling the dynamics of correlations of multiple time series is an important yet difficult task, especially when the dimension is not confined to be low. In this paper, we propose a new multivariate stochastic volatility model featuring a block correlation structure. Our specification is built upon the new parametrization of the correlation matrix of Archakov & Hansen (2021) and extends the MSV-GFT model introduced in Chen et al. (2025). A Particle Gibbs Ancestor Sampling (PGAS) method is proposed to conduct the Bayesian analysis. It is shown to perform well for our model in finite samples. An empirical application based on a dozen U.S. stocks shows that our new model outperforms alternative specifications in terms of both the in-sample performance and the out-of-sample performance.

Suggested Citation

  • Han Chen & Yijie Fei & Jun Yu, 2026. "Multivariate Stochastic Volatility Model with Block Correlations," Working Papers 202638, University of Macau, Faculty of Business Administration.
    • Handle: RePEc:boa:wpaper:202638
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    References listed on IDEAS

    as
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