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Measuring the Unmeasurable: An Application of Uncertainty Quantification to Financial Portfolios

Author

Listed:
  • Jingnan Chen

    () (Singapore University of Technology and Design)

  • Mark D. Flood

    () (Office of Financial Research)

  • Richard B. Sowers

    () (University of Illinois at Urbana-Champaign)

Abstract

We extract from the yield curve a new measure of fundamental economic uncertainty, based on McDiarmid's distance and related methods for optimal uncertainty quantification (OUQ). OUQ seeks analytical bounds on a system's behavior, even where the underlying data-generating process and system response function are incompletely specified. We use OUQ to stress test a simple fixed-income portfolio, certifying its safety—i.e., that potential losses will be "small" in an appropriate sense. The results give explicit tradeoffs between: scenario count, maximum loss, test horizon, and confidence level. Unfortunately, uncertainty peaks in late 2008, weakening certification assurances just when they are needed most.

Suggested Citation

  • 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.
  • Handle: RePEc:ofr:wpaper:15-19
    as

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    File URL: https://financialresearch.gov/working-papers/files/OFRwp-2015-19_Measuring-the-Unmeasurable.pdf
    File Function: First version, 2015
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    References listed on IDEAS

    as
    1. Beverly Hirtle & Til Schuermann & Kevin J. Stiroh, 2009. "Macroprudential supervision of financial institutions: lessons from the SCAP," Staff Reports 409, Federal Reserve Bank of New York.
    2. Antonella Foglia, 2009. "Stress Testing Credit Risk: A Survey of Authorities' Aproaches," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 9-45, September.
    3. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    4. Francis X. Diebold & Neil A. Doherty & Richard J. Herring, 2010. "The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice," Economics Books, Princeton University Press, edition 1, number 9223, October.
    5. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    6. repec:wsi:qjfxxx:v:02:y:2012:i:02:n:s2010139212500085 is not listed on IDEAS
    7. 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.
    8. Yuhong Xu, 2014. "Robust valuation and risk measurement under model uncertainty," Papers 1407.8024, arXiv.org.
    9. Hamilton, James D. & Wu, Jing Cynthia, 2012. "Identification and estimation of Gaussian affine term structure models," Journal of Econometrics, Elsevier, vol. 168(2), pages 315-331.
    10. Gregory R. Duffee, 2011. "Information in (and not in) the Term Structure," Review of Financial Studies, Society for Financial Studies, vol. 24(9), pages 2895-2934.
    11. Duffee, Gregory R., 2006. "Term structure estimation without using latent factors," Journal of Financial Economics, Elsevier, vol. 79(3), pages 507-536, March.
    12. Mark D. Flood & George G. Korenko, 2015. "Systematic scenario selection: stress testing and the nature of uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 15(1), pages 43-59, January.
    13. Darrell Duffie & Rui Kan, 1996. "A Yield-Factor Model Of Interest Rates," Mathematical Finance, Wiley Blackwell, vol. 6(4), pages 379-406.
    14. Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
    Full references (including those not matched with items on IDEAS)

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