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Nonparametric Estimation of Conditional Expected Shortfall

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  • Olivier SCAILLET

    (HEC-University of Geneva and FAME)

Abstract

We consider a nonparametric method to estimate conditional expected shortfalls, i.e. conditional expected losses knowing that losses are larger than a given loss quantile. We derive the asymptotic properties of kernal estimators of conditional expected shortfalls in the context of a stationary process satisfying strong mixing conditions. An empirical illustration is given for several stock index returns, namely CAC40, DAX30, S&P500, DJI, and Nikkei225.

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Bibliographic Info

Paper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp112.

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Date of creation: May 2004
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Handle: RePEc:fam:rpseri:rp112

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Related research

Keywords: Nonparametric; Kernel; Time series; Conditional VAR; Conditional expected shortfall; Risk management; Loss severity distribution;

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Cited by:
  1. Ivana Komunjer, 2004. "Asymmetric Power Distribution: Theory and Applications to Risk Measurement," Econometric Society 2004 Latin American Meetings 44, Econometric Society.
  2. Idier, Julien & Lamé, Gildas & Mésonnier, Jean-Stéphane, 2013. "How useful is the marginal expected shortfall for the measurement of systemic exposure? A practical assessment," Working Paper Series 1546, European Central Bank.
  3. Chun, So Yeon & Shapiro, Alexander & Uryasev, Stan, 2011. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics," MPRA Paper 30132, University Library of Munich, Germany.

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