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A Triptych Approach for Reverse Stress Testing of Complex Portfolios

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Listed:
  • Pascal Traccucci
  • Luc Dumontier
  • Guillaume Garchery
  • Benjamin Jacot

Abstract

The quest for diversification has led to an increasing number of complex funds with a high number of strategies and non-linear payoffs. The new generation of Alternative Risk Premia (ARP) funds are an example that has been very popular in recent years. For complex funds like these, a Reverse Stress Test (RST) is regarded by the industry and regulators as a better forward-looking risk measure than a Value-at-Risk (VaR). We present an Extended RST (ERST) triptych approach with three variables: level of plausibility, level of loss and scenario. In our approach, any two of these variables can be derived by providing the third as the input. We advocate and demonstrate that ERST is a powerful tool for both simple linear and complex portfolios and for both risk management as well as day-to-day portfolio management decisions. An updated new version of the Levenberg - Marquardt optimization algorithm is introduced to derive ERST in certain complex cases.

Suggested Citation

  • Pascal Traccucci & Luc Dumontier & Guillaume Garchery & Benjamin Jacot, 2019. "A Triptych Approach for Reverse Stress Testing of Complex Portfolios," Papers 1906.11186, arXiv.org.
    • Handle: RePEc:arx:papers:1906.11186
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    References listed on IDEAS

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    1. Natalie Packham & Fabian Woebbeking, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," Papers 1807.11381, arXiv.org, revised Jan 2019.
    2. Thomas Breuer & Martin Jandacka & Klaus Rheinberger & Martin Summer, 2009. "How to Find Plausible, Severe and Useful Stress Scenarios," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 205-224, September.
    3. Jules Sadefo Kamdem, 2005. "Value-At-Risk And Expected Shortfall For Linear Portfolios With Elliptically Distributed Risk Factors," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(05), pages 537-551.
    4. Paul Glasserman & Chulmin Kang & Wanmo Kang, 2013. "Stress Scenario Selection by Empirical Likelihood," Working Papers 13-07, Office of Financial Research, US Department of the Treasury.
    5. Packham, N. & Woebbeking, C.F., 2019. "A factor-model approach for correlation scenarios and correlation stress testing," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 92-103.
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