ARTICLE
ABSTRACT
Copulas have become one of most popular tools used in modelling the dependencies among financial time series. The main aim of the paper is to formally assess the relative explanatory power of competing bivariate Copula-AR-GARCH models, which differ in assumptions on the conditional dependence structure represented by particular copulas. For the sake of comparison the Copula-AR-GARCH models are estimated using the maximum likelihood method, and next they are informally compared and ranked according to the values of the Akaike (AIC) and of the Schwarz (BIC) information criteria. We apply these tools to the daily growth rates of four sub-indices of the stock index WIG published by the Warsaw Stock Exchange. Our results indicate that the informal use of the information criteria (AIC or BIC) leads to very similar ranks of models as compared to those obtained by the use of the formal Bayesian model comparison.
KEYWORDS
Copula, Copula-AR-GARCH model, Bayesian model comparison
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