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Tuesday, August 3 • 10:45am - 12:15pm
4E3 Sensitivity-implied tail-correlation matrices

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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.


Mike Hoy

Professor, University of Guelph

avatar for Sebastian Schlütter

Sebastian Schlütter

Mainz University of Applied Sciences

Tuesday August 3, 2021 10:45am - 12:15pm EDT

Attendees (7)