期刊名称：Central European Journal of Economic Modelling and Econometrics
印刷版ISSN：2080-0886
电子版ISSN：2080-119X
出版年度：2019
期号：3
页码：173-197
DOI：10.24425/cejeme.2019.130677
语种：English
出版社：Polska Akademia Nauk
摘要：Hybrid MSV-MGARCH models, in particular the MSF-SBEKK specification, proved useful in multivariate modelling of returns on financial and commodity markets. The initial MSF-MGARCH structure, called LNMSF-MGARCH here, is obtained by multiplying the MGARCH conditional covariance matrix Ht by a scalar random variable gt such that {ln gt, t ∈ Z} is a Gaussian AR(1) latent process with auto-regression parameter ϕ. Here we also consider an IG-MSF-MGARCH specification, which is a hybrid generalisation of conditionally Student t MGARCH models, since the latent process {gt} is no longer marginally log-normal (LN), but for ϕ = 0 it leads to an inverted gamma (IG) distribution for gt and to the t-MGARCH case. If ϕ 6= 0, the latent variables gt are dependent, so (in comparison to the t-MGARCH specification) we get an additional source of dependence and one more parameter. Due to the existence of latent processes, the Bayesian approach, equipped with MCMC simulation techniques, is a natural and feasible statistical tool to deal with MSF-MGARCH models. In this paper we show how the distributional assumptions for the latent process together with the specification of the prior density for its parameters affect posterior results, in particular the ones related to adequacy of the t-MGARCH model. Our empirical findings demonstrate sensitivity of inference on the latent process and its parameters, but, fortunately, neither on volatility of the returns nor on their conditional correlation. The new IG-MSF-MGARCH specification is based on a more volatile latent process than the older LN-MSF-MGARCH structure, so the new one may lead to lower values of ϕ – even so low that they can justify the popular t-MGARCH model.
