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
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Backtesting VaR and ES under the magnifying glass
AU - Argyropoulos, C.
AU - Panopoulou, E.
N1 - 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
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
KW - Backtesting
KW - Expected Shortfall
KW - Forecast evaluation
KW - Model accuracy
KW - Value-at-Risk
U2 - 10.1016/j.irfa.2019.04.005
DO - 10.1016/j.irfa.2019.04.005
M3 - Journal article
VL - 64
SP - 22
EP - 37
JO - International Review of Financial Analysis
JF - International Review of Financial Analysis
SN - 1057-5219
ER -