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Pages 43-59
Received 05 Mar 2013
Accepted 08 May 2014
Published online: 08 Aug 2014
 
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We present a technique for selecting multidimensional shock scenarios for use in financial stress testing. The methodology systematically enforces internal consistency among the shock dimensions by sampling points of arbitrary severity from a plausible joint probability distribution. The approach involves a grid search of sparse, well distributed, stress-test scenarios, which we regard as a middle ground between traditional stress testing and reverse stress testing. Choosing scenarios in this way reduces the danger of ‘blind spots’ in stress testing. We suggest extensions to address the issues of non-monotonic loss functions and univariate shocks. We provide tested and commented source code in Matlab®.

Acknowledgement

We gratefully acknowledge helpful comments from Dennis Bams, Thomas Breuer, Bertrand Candelon, Greg Feldberg, Paul Glasserman, Alan Genz, Bryan Goudie, Art Hogan, Benjamin Kay, Alexander McNeil, Jonathan Sokobin, Arthur Small, Peyton Young, two thoughtful referees and conference and seminar participants at the Federal Housing Finance Agency, a Nov. 2008 Washington DC GARP chapter meeting, the 2009 European Banking Symposium at the University of Maastricht, the 2009 meeting of the Eastern Finance Association, a 2009 Loyola University conference on Risk Management and Corporate Governance, the 2010 meeting of the Financial Management Association, the 2010 Winter Simulation Conference, and 2012 research workshops at Penn State University and at the Office of Financial Research. Any remaining errors pertain to the authors alone.