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Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall

Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall We consider a nonparametric method to estimate the expected shortfall—that is, the expected loss on a portfolio of financial assets knowing that the loss is larger than a given quantile. We derive the asymptotic properties of the kernel estimators of the expected shortfall and its first‐order derivative with respect to portfolio allocation in the context of a stationary process satisfying strong mixing conditions. An empirical illustration is given for a portfolio of stocks. Another empirical illustration deals with data on fire insurance losses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mathematical Finance Wiley

Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall

Mathematical Finance , Volume 14 (1) – Jan 1, 2004

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References (15)

Publisher
Wiley
Copyright
Copyright © 2004 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0960-1627
eISSN
1467-9965
DOI
10.1111/j.0960-1627.2004.00184.x
Publisher site
See Article on Publisher Site

Abstract

We consider a nonparametric method to estimate the expected shortfall—that is, the expected loss on a portfolio of financial assets knowing that the loss is larger than a given quantile. We derive the asymptotic properties of the kernel estimators of the expected shortfall and its first‐order derivative with respect to portfolio allocation in the context of a stationary process satisfying strong mixing conditions. An empirical illustration is given for a portfolio of stocks. Another empirical illustration deals with data on fire insurance losses.

Journal

Mathematical FinanceWiley

Published: Jan 1, 2004

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