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Stress scenario selection by empirical likelihood

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  • Paul Glasserman
  • Chulmin Kang
  • Wanmo Kang

Abstract

This paper develops a method for selecting and analysing stress scenarios for financial risk assessment, with particular emphasis on identifying sensible combinations of stresses to multiple factors. We focus primarily on reverse stress testing - finding the most likely scenarios leading to losses exceeding a given threshold. We approach this problem using a nonparametric empirical likelihood estimator of the conditional mean of the underlying market factors given large losses. We then scale confidence regions for the conditional mean by a coefficient that depends on the tails of the market factors to estimate the most likely loss scenarios. We provide rigorous justification for the confidence regions and the scaling procedure when the joint distribution of the market factors and portfolio loss is elliptically contoured. We explicitly characterize the impact of the heaviness of the tails of the distribution, contrasting a broad spectrum of cases including exponential tails and regularly varying tails. The key to this analysis lies in the asymptotics of the conditional variances and covariances in extremes. These results also lead to asymptotics for marginal expected shortfall and the corresponding variance, conditional on a market stress; we combine these results with empirical likelihood significance tests of systemic risk rankings based on marginal expected shortfall in stress scenarios.

Suggested Citation

  • Paul Glasserman & Chulmin Kang & Wanmo Kang, 2015. "Stress scenario selection by empirical likelihood," Quantitative Finance, Taylor & Francis Journals, vol. 15(1), pages 25-41, January.
  • Handle: RePEc:taf:quantf:v:15:y:2015:i:1:p:25-41
    DOI: 10.1080/14697688.2014.926019
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    File URL: http://hdl.handle.net/10.1080/14697688.2014.926019
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    References listed on IDEAS

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    1. Til Schuermann & Beverly Hirtle & Kevin J. Stiroh, 2009. "Macroprudential supervision of financial institutions: lessons from the SCAP," Staff Reports 409, Federal Reserve Bank of New York.
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    Cited by:

    1. Gary Gorton, 2015. "Stress for Success: A Review of Timothy Geithner's Financial Crisis Memoir," Journal of Economic Literature, American Economic Association, vol. 53(4), pages 975-995, December.
    2. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    3. Caio Almeida & Kym Ardison & René Garcia & Jose Vicente, 2017. "Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 15(3), pages 333-376.
    4. repec:eee:jbfina:v:101:y:2019:i:c:p:92-103 is not listed on IDEAS
    5. Jingnan Chen & Mark D. Flood & Richard B. Sowers, 2015. "Measuring the Unmeasurable: An Application of Uncertainty Quantification to Financial Portfolios," Working Papers 15-19, Office of Financial Research, US Department of the Treasury.
    6. Natalie Packham & Fabian Woebbeking, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," Papers 1807.11381, arXiv.org, revised Jan 2019.
    7. Dimitri G Demekas, 2015. "Designing Effective Macroprudential Stress Tests; Progress So Far and the Way Forward," IMF Working Papers 15/146, International Monetary Fund.

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