Nonparametric Estimation of Conditional Expected Shortfall
AbstractWe 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 InfoPaper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp112.
Date of creation: May 2004
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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 and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- 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; Micro Finance Institutions; Mortgages
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-04-16 (All new papers)
- NEP-ECM-2005-04-16 (Econometrics)
- NEP-FIN-2005-04-16 (Finance)
- NEP-RMG-2005-04-16 (Risk Management)
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