Multivariate Stochastic Volatility with Co-Heteroscedasticity

Joshua Chan, Doucet Arnaud, León-González Roberto () and Strachan Rodney W.
Additional contact information
Doucet Arnaud: University of Oxford, Oxford, England
León-González Roberto: National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan
Strachan Rodney W.: University of Queensland, Brisbane, Australia

Studies in Nonlinear Dynamics & Econometrics, 2025, vol. 29, issue 3, 265-300

Abstract: A new methodology that decomposes shocks into homoscedastic and heteroscedastic components is developed. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity; a property known as co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which is shown to be important for volatility estimation. By incorporating testable co-heteroscedasticity restrictions, the specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle filter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternating-order particle Gibbs that reduces the amount of particles needed for accurate estimation. An empirical application to a large Vector Autoregression (VAR) is provided, finding strong evidence for co-heteroscedasticity and that the new method outperforms some previously proposed methods in terms of forecasting at all horizons. It is also found that the structural monetary shock is 98.8 % homoscedastic, and that investment and the SP 500 index are nearly 100 % determined by fat tail heteroscedastic shocks. A Monte Carlo experiment illustrates that the new method estimates well the characteristics of approximate factor models with heteroscedastic errors.

Keywords: Markov chain Monte Carlo; Gibbs sampling; flexible parametric model; particle filter; co-heteroscedasticity; state-space (search for similar items in EconPapers)
JEL-codes: C11 C15 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/snde-2023-0056 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Multivariate Stochastic Volatility with Co-Heteroscedasticity (2020)
Working Paper: Multivariate stochastic volatility with co-heteroscedasticity (2018)
Working Paper: Multivariate Stochastic Volatility with Co-Heteroscedasticity (2018)
Working Paper: Multivariate Stochastic Volatility with Co-Heteroscedasticity (2018)
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:bpj:sndecm:v:29:y:2025:i:3:p:265-300:n:1003

Ordering information: This journal article can be ordered from
https://www.degruyte ... ournal/key/snde/html

DOI: 10.1515/snde-2023-0056

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().