Subsampling the distribution of diverging statistics with applications to finance

Patrice Bertail; Christian Haefke; Dimitris N. Politis; Halbert White

doi:10.1016/S0304-4076(03)00215-X

Subsampling the distribution of diverging statistics with applications to finance

Patrice Bertail, Christian Haefke, Dimitris N. Politis, Halbert White

Economics

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.

Original language	English (US)
Pages (from-to)	295-326
Number of pages	32
Journal	Journal of Econometrics
Volume	120
Issue number	2
DOIs	https://doi.org/10.1016/S0304-4076(03)00215-X
State	Published - Jun 2004

Keywords

Extreme value statistics
Portfolio selection
Resampling methods
Value at Risk

ASJC Scopus subject areas

Economics and Econometrics

Access to Document

10.1016/S0304-4076(03)00215-X

Cite this

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title = "Subsampling the distribution of diverging statistics with applications to finance",

abstract = "In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.",

keywords = "Extreme value statistics, Portfolio selection, Resampling methods, Value at Risk",

author = "Patrice Bertail and Christian Haefke and Politis, {Dimitris N.} and Halbert White",

note = "Funding Information: We thank Bob Trippi for the provision of computer resources to perform the numerically intensive tasks of Section 5 . We gratefully acknowledge very helpful comments from the anonymous referees. P. Bertail acknowledges financial support from INRA (AIP 2000–2002 directed by Ph. Verger). Haefke acknowledges financial support from EU grant HPMF-CT-2001-01252 and CICYT. White's participation was supported by NSF grants SBR 9811562 and SES 0111238. ",

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doi = "10.1016/S0304-4076(03)00215-X",

language = "English (US)",

volume = "120",

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T1 - Subsampling the distribution of diverging statistics with applications to finance

AU - Bertail, Patrice

AU - Haefke, Christian

AU - Politis, Dimitris N.

AU - White, Halbert

N1 - Funding Information: We thank Bob Trippi for the provision of computer resources to perform the numerically intensive tasks of Section 5 . We gratefully acknowledge very helpful comments from the anonymous referees. P. Bertail acknowledges financial support from INRA (AIP 2000–2002 directed by Ph. Verger). Haefke acknowledges financial support from EU grant HPMF-CT-2001-01252 and CICYT. White's participation was supported by NSF grants SBR 9811562 and SES 0111238.

PY - 2004/6

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N2 - In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.

AB - In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.

KW - Extreme value statistics

KW - Portfolio selection

KW - Resampling methods

KW - Value at Risk

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JO - Journal of Econometrics

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Subsampling the distribution of diverging statistics with applications to finance

Abstract

Keywords

ASJC Scopus subject areas

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