Multivariate Stochastic Volatility Models based on Generalized Fisher Transformation

Han Chen (), Yijie Fei () and Jun Yu
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Han Chen: Hunan University
Yijie Fei: Hunan University

No 202419, Working Papers from University of Macau, Faculty of Business Administration

Abstract: Modeling multivariate stochastic volatility (MSV) can pose significant challenges, particularly when both variances and covariances are time-varying. In this study, we tackle these complexities by introducing novel MSV models based on the generalized Fisher transformation (GFT) proposed by Archakov and Hansen (2021). Our model exhibits remarkable flexibility, ensuring the positive-definiteness of the variancecovariance matrix, and disentangling the driving forces of volatilities and correlations. To conduct Bayesian analysis of the models, we employ a Particle Gibbs Ancestor Sampling (PGAS) method, facilitating efficient Bayesian model comparisons. Furthermore, we extend our MSV model to cover leverage effects and incorporate realized measures. Our simulation studies demonstrate that the proposed method performs well for our GFT-based MSV model. Furthermore, empirical studies based on equity returns show that the MSV models outperform alternative specifications in both in-sample and outof-sample performances.

Keywords: Multivariate stochastic volatility; Dynamic correlation; Leverage effect; Particle filter; Markov chain Monte Carlo; Realized measures (search for similar items in EconPapers)
JEL-codes: C12 C32 C53 C58 G10 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2024-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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Published in UM-FBA Working Paper Series

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