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A factor-model approach for correlation scenarios and correlation stress testing

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  • Packham, N.
  • Woebbeking, C.F.

Abstract

In 2012, JPMorgan accumulated a USD 6.2 billion loss on a credit derivatives portfolio, the so-called “London Whale”, partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for scenario-based stress testing of correlations. We derive a number of analytical results related to a portfolio of homogeneous assets. Using the concept of Mahalanobis distance, we show how to identify adverse scenarios of correlation risk. In addition, we demonstrate how correlation and volatility stress tests can be combined. As an example, we apply the factor-model approach to the “London Whale” portfolio and determine the value-at-risk impact from correlation changes. Since our findings are particularly relevant for large portfolios, where even small correlation changes can have a large impact, a further application would be to stress test portfolios of central counterparties, which are of systemically relevant size.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jbfina:v:101:y:2019:i:c:p:92-103
    DOI: 10.1016/j.jbankfin.2019.01.020
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    Citations

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    Cited by:

    1. Packham, Natalie & Woebbeking, Fabian, 2021. "Correlation scenarios and correlation stress testing," IRTG 1792 Discussion Papers 2021-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. N. Packham & F. Woebbeking, 2021. "Correlation scenarios and correlation stress testing," Papers 2107.06839, arXiv.org, revised Sep 2022.
    3. Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio," Papers 2002.02008, arXiv.org, revised Sep 2020.
    4. Fabian Woebbeking, 2021. "Cryptocurrency volatility markets," Digital Finance, Springer, vol. 3(3), pages 273-298, December.
    5. Packham, N. & Woebbeking, F., 2023. "Correlation scenarios and correlation stress testing," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 55-67.
    6. Pilkington, Marc, 2022. "The London Whale Scandal under new Scrutiny," International Review of Financial Analysis, Elsevier, vol. 80(C).
    7. Chibane, Messaoud & Gabriel, Amadeus & Giménez Roche, Gabriel A., 2022. "Credit booms and crisis-emergent asset comovement: The problem of latent correlation," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 270-279.
    8. Pascal Traccucci & Luc Dumontier & Guillaume Garchery & Benjamin Jacot, 2019. "A Triptych Approach for Reverse Stress Testing of Complex Portfolios," Papers 1906.11186, arXiv.org.
    9. Aigner, Philipp & Schlütter, Sebastian, 2023. "Enhancing gradient capital allocation with orthogonal convexity scenarios," ICIR Working Paper Series 47/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).

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    More about this item

    Keywords

    Correlation stress testing; Scenario selection; Market risk; “London Whale”;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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