Balter, Janine and McNeil, Alexander John orcid.org/0000-0002-6137-2890 (2024) Multivariate Spectral Backtests of Forecast Distributions under Unknown Dependencies. Risks. 13. ISSN 2227-9091
Abstract
Under the revised market risk framework of the Basel Committee on Banking Supervision, the model validation regime for internal models now requires that models capture the tail risk in profit-and-loss (P&L) distributions at the trading desk level. We develop multi-desk backtests, which simultaneously test all trading desk models and which exploit all the information available in the presence of an unknown correlation structure between desks. We propose a multi-desk extension of the spectral test of Gordy and McNeil, which allows the evaluation of a model at more than one confidence level and contains a multi-desk value-at-risk (VaR) backtest as a special case. The spectral tests make use of realised probability integral transform values based on estimated P&L distributions for each desk and are more informative and more powerful than simpler tests based on VaR violation indicators. The new backtests are easy to implement with a reasonable running time; in a series of simulation studies, we show that they have good size and power properties.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 by the authors |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > The York Management School |
Depositing User: | Pure (York) |
Date Deposited: | 18 Jan 2024 10:50 |
Last Modified: | 19 Nov 2024 00:45 |
Published Version: | https://doi.org/10.3390/risks12010013 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3390/risks12010013 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207991 |
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Filename: risks-12-00013.pdf
Description: Multivariate Spectral Backtests of Forecast Distributions under Unknown Dependencies
Licence: CC-BY 2.5