Evaluating the Precision of Estimators of Quantile-Based Risk Measures

John Cotter and Kevin Dowd ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper examines the precision of estimators of Quantile-Based Risk Measures (Value at Risk, Expected Shortfall, Spectral Risk Measures). It first addresses the question of how to estimate the precision of these estimators, and proposes a Monte Carlo method that is free of some of the limitations of existing approaches. It then investigates the distribution of risk estimators, and presents simulation results suggesting that the common practice of relying on asymptotic normality results might be unreliable with the sample sizes commonly available to them. Finally, it investigates the relationship between the precision of different risk estimators and the distribution of underlying losses (or returns), and yields a number of useful conclusions.

JEL-codes: G00 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://mpra.ub.uni-muenchen.de/3504/1/MPRA_paper_3504.pdf original version (application/pdf)

Related works:
Working Paper: Evaluating the Precision of Estimators of Quantile-Based Risk Measures (2011)
Working Paper: Evaluating the Precision of Estimators of Quantile-Based Risk Measures (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:3504

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