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Conditional value-at-risk: Aspects of modeling and estimation

Author

Listed:
  • Len Umantsev

    (Department of Management Science and Engineering, Stanford University, Stanford, CA 94305-4026)

  • Victor Chernozhukov

    (Department of Economics, MIT, Cambridge, MA 02139)

Abstract

This paper considers flexible conditional (regression) measures of market risk. Value-at-Risk modeling is cast in terms of the quantile regression function - the inverse of the conditional distribution function. A basic specification analysis relates its functional forms to the benchmark models of returns and asset pricing. We stress important aspects of measuring the extremal and intermediate conditional risk. An empirical application characterizes the key economic determinants of various levels of conditional risk.

Suggested Citation

  • Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
    • Handle: RePEc:spr:empeco:v:26:y:2001:i:1:p:271-292
    Note: Received: September 30, 1999/Revised version: November 20, 2000
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    More about this item

    Keywords

    Value-at-Risk · Quantiles · Extreme Value Theory;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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