Hybrid MSV-MGARCH Models – General Remarks and the GMSF-SBEKK Specification
Jacek Osiewalski and
Krzysztof Osiewalski
Central European Journal of Economic Modelling and Econometrics, 2016, vol. 8, issue 4, 241-271
Abstract:
The first so-called hybrid MSV-MGARCH models were characterized by the conditional covariance matrix that was a product of a univariate latent process and a matrix with a simple MGARCH structure (Engle’s DCC or scalar BEKK). The aim was to parsimoniously describe volatility of a large group of assets. The proposed hybrid models, similarly as pure MSV specifications (and other models based on latent processes), required the Bayesian approach equipped with efficient MCMC simulation tools. The numerical effort has payed – the hybrid models seem particularly useful due to their good fit and ability to jointly cope with large portfolios. In particular, the simplest hybrid, now called the MSF-SBEKK model, has been successfully used in many applications. However, one latent process may be insufficient in the case of a highly heterogeneous portfolio. Thus, in this study we discuss a general hybrid MSV-MGARCH model structure, showing its basic characteristics that explain greater flexibility of such hybrid structure with respect to the corresponding MGARCH class. From the empirical perspective, we advocate the GMSF-SBEKK specification, which uses as many latent processes as there are relatively homogeneous groups of assets. We present full Bayesian inference for such models, with the use of an efficient MCMC simulation strategy. The approach is used to jointly model volatility on very different markets. Joint modelling is formally compared to individual modelling of volatility on each market.
Keywords: Bayesian econometrics; multivariate volatility models; MGARCH processes; MSV processes; financial markets; commodity markets (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 C58 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://cejeme.org/publishedarticles/2016-29-06-636166241511250000-4258.pdf (application/pdf)
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:psc:journl:v:8:y:2016:i:4:p:241-271
Access Statistics for this article
More articles in Central European Journal of Economic Modelling and Econometrics from Central European Journal of Economic Modelling and Econometrics
Bibliographic data for series maintained by Damian Jelito ().