Quantifying and correcting the bias in estimated risk measures

Joseph H.T. Kim, Mary R. Hardy

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

In this paper we explore the bias in the estimation of the Value at Risk and Conditional Tail Expectation risk measures using Monte Carlo simulation. We assess the use of bootstrap techniques to correct the bias for a number of different examples. In the case of the Conditional Tail Expectation, we show that application of the exact bootstrap can improve estimates, and we develop a practical guideline for assessing when to use the exact bootstrap.

Original languageEnglish
Pages (from-to)365-386
Number of pages22
JournalASTIN Bulletin
Volume37
Issue number2
DOIs
Publication statusPublished - 2007 Nov 1

Fingerprint

Bootstrap
Risk measures
Conditional tail expectation
Monte Carlo simulation
Value at risk

All Science Journal Classification (ASJC) codes

  • Accounting
  • Economics and Econometrics
  • Finance

Cite this

Kim, Joseph H.T. ; Hardy, Mary R. / Quantifying and correcting the bias in estimated risk measures. In: ASTIN Bulletin. 2007 ; Vol. 37, No. 2. pp. 365-386.
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Quantifying and correcting the bias in estimated risk measures. / Kim, Joseph H.T.; Hardy, Mary R.

In: ASTIN Bulletin, Vol. 37, No. 2, 01.11.2007, p. 365-386.

Research output: Contribution to journalArticle

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