Multivariate stochastic volatility using the HESSIAN method

William McCausland (), Shirley Miller and Denis Pelletier

Econometrics and Statistics, 2021, vol. 17, issue C, 76-94

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.

Keywords: Bayesian analysis; Factor models; MCMC; State space models (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S245230622030068X
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:17:y:2021:i:c:p:76-94

DOI: 10.1016/j.ecosta.2020.07.002

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().