Truncated regular vines in high dimensions with applications to financial data

Using only bivariate copulas as building blocks, regular vines constitute a exible class of high-dimensional dependency models. However, the exibility 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 fiancial data set of Norwegian and international market variables.     «

Using only bivariate copulas as building blocks, regular vines constitute a exible class of high-dimensional dependency models. However, the exibility 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...     »