Measurement of aggregate risk with copulas

Summary When aggregating financial risk on a portfolio level, the specification of the dependence structure between the risk factors plays an important role. Promising parametric models are often based on a so-called copula approach. Case studies of market crashes suggest the application of concepts allowing for extremal dependence. We present a transformed copula as a new model that both fits the data and allows for exact prediction in the tails. It turns out that the new model improves benchmark models like the t- or Clayton copula with respect to risk measures like VaR or Expected Shortfall. By performing different goodness-of-fit tests, the quality of the estimation is examined.