Bayesian approach for mixture copula model - Sukrit Thongkairat (2019)
© Springer Nature Switzerland AG 2019. This paper aims to use the Bayesian estimation as an alternative method for formulating and estimating mixed copula models. This method has claimed to be more efficient than the conventional maximum likelihood estimator as it can deal with the high dimension copula and large parameter estimates under limited sample. In this study, we present various mixed copula functions constructed from both Elliptical and Archimedean copulas. We employ a simulation study to investigate the performance of this estimator for comparison with the maximum likelihood estimator. The results show that the Bayesian estimation is considerably more accurate than maximum likelihood estimator in various scenarios. Finally, we extend the Bayesian mixed copula to the real data and show that our approach perform well in this real data analysis.