Welcome to the ARIA 2021 Annual Meeting Agenda App. You are seeing this in simple view. Click on any session to see Presenters, Discussants and a Session Description. Click on Presenters to find a single presenter and their sessions. If you find an error, please email gphillips@aria.org. Registrants have been added and will continue to be updated until 7/29/21 when registration closes. Zoom links will be shared directly with all participants two days prior to the conference. Please make notifications@sched.com a "safe sender" in your email system. We will be sending messages throughout the conference through this medium.
Sebastian Schlütter, Mainz University of Applied Sciences; Joachim Paulusch, R+V Lebensversicherung AG
Tail-correlation matrices are an important tool for aggregating risk measurements across risk categories, asset classes and/or business segments. This paper demonstrates that the classical concepts, such as Value-at-Risk implied tail-correlations, can lead to substantial biases of the aggregate risk measurement's sensitivities with respect to risk exposures. Due to these biases, decision-makers receive an odd view of the effects of portfolio changes and may be unable to identify the optimal portfolio from a risk-return perspective. To overcome these issues, we introduce the "sensitivity-implied tail-correlation matrix". The proposed tail-correlation matrix allows for a simple deterministic risk aggregation approach which reasonably approximates the true aggregate risk measurement according to the complete multivariate risk distribution. Numerical examples demonstrate that our approach is a better basis for portfolio optimization than the Value-at-Risk implied tail-correlation matrix, especially if the calibration portfolio (or current portfolio) deviates from the optimal portfolio.
