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A Subsampling Approach to Estimating the Distribution of Diversing Statistics with Application to Assessing Financial Market Risks

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Author Info
Patrice Bertail
Christian Haefke
Dimitris N. Politis
Halbert White

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Abstract

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known rates when the underlying time series in strictly stationary abd strong mixing. Based on our results we provide a detailed discussion 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 portofolio selection.

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File URL: http://www.econ.upf.edu/docs/papers/downloads/599.pdf
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Publisher Info
Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 599.

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Date of creation: Dec 2001
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Handle: RePEc:upf:upfgen:599

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Web page: http://www.econ.upf.edu/

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Related research
Keywords: Resampling methods; extreme value statistics; value at risk; portofolio selection;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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  1. Andreas Gottschling & Christian Haefke & Halbert White, 1999. "Closed Form Integration of Artificial Neural Networks with Some Applications to Finance," University of California at San Diego, Economics Working Paper Series 1999-24, Department of Economics, UC San Diego. [Downloadable!]
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