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Evaluating the precision of estimators of quantile-based risk measures
Author(s)
Date Issued
2007-05
Date Available
2009-06-15T15:36:28Z
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.
Sponsorship
Economic and Social Research Council; University College Dublin. School of Business
Type of Material
Working Paper
Publisher
University College Dublin. School of Business. Centre for Financial Markets
University College Dublin. School of Business
Series
Centre for Financial Markets working paper series
WP-07-13
UCD Business Schools Working Paper Series
WP08/17
Copyright (Published Version)
2007, Centre for Financial Markets
Classification
G15
Subject – LCSH
Risk--Econometric models
International finance
Monte Carlo method
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
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