Estimating the variance of bootstrapped Risk measures

Joseph H.T. Kim, Mary R. Hardy

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In Kim and Hardy (2007) the exact bootstrap was used to estimate certain risk measures including Value at Risk and the Conditional Tail Expectation. In this paper we continue this work by deriving the influence function of the exact-bootstrapped quantile risk measure. We can use the influence function to estimate the variance of the exact-bootstrap risk measure. We then extend the result to the L-estimator class, which includes the conditional tail expectation risk measure. The resulting formula provides an alternative way to estimate the variance of the bootstrapped risk measures, or the whole L-estimator class in an analytic form. A simulation study shows that this new method is comparable to the ordinary resampling-based bootstrap method, with the advantages of an analytic approach.

Original languageEnglish
Pages (from-to)199-223
Number of pages25
JournalASTIN Bulletin
Volume39
Issue number1
DOIs
Publication statusPublished - 2009 Dec 1

Fingerprint

Risk measures
Measure of risk
Conditional tail expectation
Bootstrap
Influence function
Estimator
Bootstrap method
Value at risk
Resampling
Quantile
Simulation study

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Economics and Econometrics

Cite this

Kim, Joseph H.T. ; Hardy, Mary R. / Estimating the variance of bootstrapped Risk measures. In: ASTIN Bulletin. 2009 ; Vol. 39, No. 1. pp. 199-223.
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Estimating the variance of bootstrapped Risk measures. / Kim, Joseph H.T.; Hardy, Mary R.

In: ASTIN Bulletin, Vol. 39, No. 1, 01.12.2009, p. 199-223.

Research output: Contribution to journalArticle

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