A subsampling approach to estimating the distribution of diversing statistics with application to assessing financial market risks
AbstractIn 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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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 599.
Date of creation: Dec 2001
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Web page: http://www.econ.upf.edu/
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 and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-02-10 (All new papers)
- NEP-ECM-2002-02-22 (Econometrics)
- NEP-FMK-2002-02-15 (Financial Markets)
- NEP-IAS-2002-02-15 (Insurance Economics)
- NEP-PKE-2002-02-15 (Post Keynesian Economics)
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- Victor Chernozhukov & Iván Fernández-Val, 2011.
"Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks,"
Review of Economic Studies,
Oxford University Press, vol. 78(2), pages 559-589.
- Victor Chernozhukov & Iván Fernández-Val, 2011. "Inference for extremal conditional quantile models, with an application to market and birthweight risks," CeMMAP working papers CWP40/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Ivan Fernandez-Val, 2009. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," Papers 0912.5013, arXiv.org.
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