Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235866 
Year of Publication: 
2021
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2021-012
Publisher: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Abstract: 
We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or highest density regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.
Subjects: 
Correlation stress testing
reverse stress testing
factor selection
scenario selection
Bayesian variable selection
market risk management
JEL: 
G11
G32
Document Type: 
Working Paper

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