Evaluating the precision of estimators of quantile-based risk measures

Kevin Dowd and John Cotter

Centre for Financial Markets Working Papers from Research Repository, University College Dublin

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.

Keywords: value at risk; Expected shortfall; Spectral risk measures; Moments; Precision; Risk--Econometric models; International finance; Monte Carlo method (search for similar items in EconPapers)
JEL-codes: G15 (search for similar items in EconPapers)
Date: 2007-05
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10197/1191 First version, 2007 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rru:cfmwps:10197/1191

Access Statistics for this paper

More papers in Centre for Financial Markets Working Papers from Research Repository, University College Dublin Contact information at EDIRC.
Bibliographic data for series maintained by Joseph Greene ().