@ARTICLE{Mokrzycka_Justyna_Bayesian_2019, author={Mokrzycka, Justyna}, number={No 1}, journal={Central European Journal of Economic Modelling and Econometrics}, pages={47-71}, howpublished={online}, year={2019}, publisher={Oddział PAN w Łodzi}, abstract={The aim of the study is to formally compare the explanatory power of Copula-GARCH and MGARCH models. The models are estimated for logarithmic daily rates of return of two exchange rates: EUR/PLN, USD/PLN and stock market indices: SP500, BUX. The analysis is performed within the Bayesian framework. The posterior model probabilities point to AR(1)-tSBEKK(1,1) for the exchange rates and VAR(1)-tCopula-GARCH(1,1) for the stock market indices, as the superior specifications. If the marginal sampling distributions are different in terms of tail thickness, the Copula-GARCH models have higher explanatory power than the MGARCH models.}, type={Artykuły / Articles}, title={Bayesian Comparison of Bivariate Copula-GARCH and MGARCH Models}, URL={http://journals.pan.pl/Content/112902/PDF-MASTER/mainFile.pdf}, doi={10.24425/cejeme.2019.129362}, keywords={Bayesian model comparison, Copula-GARCH model, Multivariate GARCH model, Monte Carlo Importance Sampling}, }