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Multivariate stochastic volatility using the HESSIAN method

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  • McCausland, William
  • Miller, Shirley
  • Pelletier, Denis

Abstract

A new method is proposed for the analysis of multivariate stochastic volatility models, based on efficient draws of volatility from its conditional posterior distribution. It applies to models with several kinds of cross-sectional dependence. Full autoregression and covariance matrices imply dependent volatility series. Mean factor structure allows conditional correlations to vary in time and covary with conditional variances; factors are conditionally Student’s t, allowing for tail dependence across assets, with factor-specific degrees of freedom. Given factors, returns have heterogeneous Student’s t marginals; a copula completes their joint distribution. Volatility series are drawn as a block, one series at a time. An application using daily returns data for ten currencies shows that all features of the model are important.

Suggested Citation

  • McCausland, William & Miller, Shirley & Pelletier, Denis, 2021. "Multivariate stochastic volatility using the HESSIAN method," Econometrics and Statistics, Elsevier, vol. 17(C), pages 76-94.
    • Handle: RePEc:eee:ecosta:v:17:y:2021:i:c:p:76-94
    DOI: 10.1016/j.ecosta.2020.07.002
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