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Nonparametric Estimation of Conditional Expected Shortfall

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Author Info
Olivier SCAILLET (HEC-University of Geneva and FAME)

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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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Publisher 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;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages
G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies
G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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  1. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921. [Downloadable!]
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This page was last updated on 2009-11-19.

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