Truncated regular vines in high dimensions with application to financial data

Using only bivariate copulas as building blocks, regular vines constitute a flexible class of high-dimensional dependency models. However, the flexibility comes along with an exponentially increasing complexity in larger dimensions. In order to counteract this problem, we propose using statistical model selection techniques to either truncate or simplify a regular vine. As a special case, we consider the simplification of a canonical vine using a multivariate copula as previously treated by Heinen and Valdesogo (2009) and Robles (2009). We validate the proposed approaches by extensive simulation studies and use them to investigate a 19-dimensional financial data set of Norwegian and international market variables.