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  • Backtesting_Final

    Rights statement: This is the author’s version of a work that was accepted for publication in International Review of Financial Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Review of Financial Analysis, 65, 2019 DOI: 10.1016/j.irfa.2019.04.005

    Accepted author manuscript, 949 KB, PDF-document

    Embargo ends: 2/11/20

    Available under license: CC BY-NC-ND

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Backtesting VaR and ES under the magnifying glass

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>1/07/2019
<mark>Journal</mark>International Review of Financial Analysis
Volume64
Number of pages16
Pages (from-to)22-37
Publication statusPublished
Early online date2/05/19
Original languageEnglish

Abstract

Backtesting provides the means of determining the accuracy of risk forecasts and the corresponding risk model. Given that the actual return generating process is unknown, the evaluation methods rely on various assumptions in order to quantify the models inefficiencies and proceed with the model evaluation. These method specific assumptions, in conjunction with the regulatory policies can introduce distortions in the evaluation process, which affect the reliability of the evaluation results. To investigate such effects from a practitioner's perspective, this paper reviews the major Value at Risk and Expected Shortfall forecast evaluation methods and evaluates their performance under a common simulation and financial application framework. Our findings suggest that focusing on specific individual hypothesis tests provides a more reliable alternative than the corresponding conditional coverage ones. In addition, selecting a two-year out-of-sample period provides a significantly better power to relevance ratio than the more relevant but powerless regulatory one-year specification.

Bibliographic note

This is the author’s version of a work that was accepted for publication in International Review of Financial Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Review of Financial Analysis, 65, 2019 DOI: 10.1016/j.irfa.2019.04.005