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A Subsampling Approach to Estimating The Distribution of Diverging Statistics with Applications to Assessing Financial Market Risk

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  • Bertail, Patrice
  • Haefke, Christian
  • Politis, D N
  • White, Halbert

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 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.

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File URL: http://www.escholarship.org/uc/item/1nk340cd.pdf;origin=repeccitec
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Bibliographic Info

Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt1nk340cd.

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Date of creation: 01 Jan 2000
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Handle: RePEc:cdl:ucsdec:qt1nk340cd

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Keywords: resampling methods; extreme value statistics; value at risk; portfolio selection;

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