Copula Model Selection of Stock Return Time Series Using Information Complexity

Abstract

In this paper we estimate the correlation between four different stock return prices. To accomplish this, we use the copula models to study the dependency structure between the variables. The original variables of interest are mapped into more manageable variables by considering joint and marginal distributions of these variables. Then a correlational structure between these variables are obtained. We fit several well-known copula models to the portfolio of the stock return price dataset using consistent information complexity (CICOMP) criterion along with other AIC-type criteria to choose the best copula functional model. CICOMP predominated the AIC-type criterion, both in the case when the fitted models are correctly specified. We expect to get more realistic results using other copula distributions contrary to the Gaussian copula used by Li (2000) that fails to capture the dependence between extreme events.