Multivariate Stochastic Volatility Model with Block Correlations

Han Chen (), Yijie Fei () and Jun Yu
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Han Chen: College of Finance and Statistics, Hunan University
Yijie Fei: College of Finance and Statistics, Hunan University

No 202638, Working Papers from University of Macau, Faculty of Business Administration

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.

Keywords: Block correlation matrix; Generalized Fisher transformation; Markov chain Monte Carlo; Multivariate stochastic volatility; Particle filter (search for similar items in EconPapers)
Pages: 44 pages
Date: 2026-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Published in UM-FBA Working Paper Series

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