Evaluating the precision of estimators of quantile-based risk measures

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Title: Evaluating the precision of estimators of quantile-based risk measures
Authors: Dowd, Kevin
Cotter, John
Permanent link: http://hdl.handle.net/10197/1191
Date: May-2007
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.
Funding Details: 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/Report no.: 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
Keywords: value at riskExpected shortfallSpectral risk measuresMomentsPrecision
Subject LCSH: Risk--Econometric models
International finance
Monte Carlo method
Other versions: http://www.ucd.ie/bankingfinance/docs/wp/WP-07-13.pdf
Language: en
Status of Item: Not peer reviewed
Appears in Collections:Centre for Financial Markets Working Papers

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