TY - JOUR N2 - 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 LN-MSF-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 alsoconsider 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 =/ 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 thet-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. L1 - http://journals.pan.pl/Content/114117/PDF-MASTER/mainFile.pdf L2 - http://journals.pan.pl/Content/114117 PY - 2019 IS - No 3 EP - 197 DO - 10.24425/cejeme.2019.130677 KW - Bayesian econometrics KW - Gibbs sampling KW - time-varying volatility KW - multivariate GARCH processes KW - multivariate SV processes A1 - Osiewalski, Jacek A1 - Pajor, Anna PB - Oddział PAN w Łodzi DA - 30.09.2019 T1 - On Sensitivity of Inference in Bayesian MSF-MGARCH Models SP - 173 UR - http://journals.pan.pl/dlibra/publication/edition/114117 T2 - Central European Journal of Economic Modelling and Econometrics ER -