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Beyond the Sample: Extreme Quantile and Probability Estimation Author info | Abstract | Publisher info | Download info | Related research | Statistics Jon Danielsson ()
Casper G. de Vries
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Economic problems such as large claims analysis in insurance and value-at-risk in finance, require assessment of the probability P of extreme realizations Q. This paper provides a semi-parametric method for estimation of extreme (P,Q) combinations for data with heavy tails. We solve the long standing problem of estimating the sample threshold of where the tail of the distribution starts. This is accomplished by the combination of a control variate type device and a subsample bootstrap technique. The subsample bootstrap attains convergence in probability, whereas the full sample bootstrap would only provide convergence in distribution. This permits a complete and comprehensive treatment of extreme (P,Q) estimation.
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Paper provided by Financial Markets Group in its series FMG Discussion Papers with number
dp298.
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Date of creation: Jul 1998Date of revision:
Handle: RePEc:fmg:fmgdps:dp298Contact details of provider: Web page: http://fmg.lse.ac.uk/
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: Phillip Kearns & Adrian Pagan, 1997.
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Tsourti, Zoi & Panaretos, John, 2003.
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Other versions: Robert Engle & Simone Manganelli, 2000.
"CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles ,"
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Robert F. Engle & Simone Manganelli, 1999.
"CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles ,"
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[Downloadable!] Robert F. Engle & Simone Manganelli, 2004.
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[Downloadable!] (restricted) Robert F. Engle & Simone Manganelli, 1999.
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Carmela E. Quintos & Zhenhong Fan & Peter C.B. Phillips, 2000.
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R.W.J. van den Goorbergh, 1999.
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Lucas, Andr‚ & Straetmans, Stefan & Klaassen, Pieter, 1999.
"Tail behavior of credit loss distributions ,"
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