Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246066 
Year of Publication: 
2021
Series/Report no.: 
ICIR Working Paper Series No. 33/19
Version Description: 
This version: 7th August 2021
Publisher: 
Goethe University Frankfurt, International Center for Insurance Regulation (ICIR), Frankfurt a. M.
Abstract: 
Tail-correlation matrices are an important tool for aggregating risk measurements across risk categories, asset classes and/or business segments. This paper demonstrates that traditional tail-correlation matriceshich are conventionally assumed to have ones on the diagonalan 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.
Subjects: 
Risk aggregation
Capital allocation
Portfolio optimization
JEL: 
G22
G28
G32
Document Type: 
Working Paper

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