Nonparametric Estimation of Conditional Expected Shortfall

Olivier Scaillet

FAME Research Paper Series from International Center for Financial Asset Management and Engineering

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.

Keywords: Nonparametric; Kernel; Time series; Conditional VAR; Conditional expected shortfall; Risk management; Loss severity distribution (search for similar items in EconPapers)
JEL-codes: C14 D81 G10 G21 G22 G28 (search for similar items in EconPapers)
Date: 2004-05
New Economics Papers: this item is included in nep-ecm, nep-fin and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.swissfinanceinstitute.ch/rp112.pdf
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://www.swissfinanceinstitute.ch/rp112.pdf [301 Moved Permanently]--> https://www.sfi.ch/rp112.pdf [302 Found]--> https://www.sfi.ch/en/rp112.pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fam:rpseri:rp112

Access Statistics for this paper

More papers in FAME Research Paper Series from International Center for Financial Asset Management and Engineering Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().