Title: Tail-risk aggregation
Authors: Dennis Mao - University of Munich (Germany)
Stefan Mittnik - University of Munich (Germany) [presenting]
Abstract: Risk aggregation is a major challenge when assessing diversified investments. Although there is ample empirical evidence that returns on financial assets correlate more strongly in down-markets and despite the growing tendency to allow for asymmetry by adopting downside-risk measures, conventional Pearson correlation still prevails in risk aggregation. We propose an alternative approach to deriving tail correlation and risk aggregation matrices associated with specific regions in joint return distributions. Specifically, we focus on tail areas to derive correlations for aggregating component risk measured in terms of expected shortfall. An empirical study illustrates that the approach can capture complex dependence structures, such as correlational asymmetry, and reliably aggregate tail risk.