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Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context

Cheng, Jie

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Abstract

Scoring rules are commonly applied to assess the accuracy of density forecasts in both univariate and multivariate settings. In a financial risk management context, we are mostly interested in a particular region of the density: the (left) tail of a portfolio's return distribution. The dependence structure between returns on different assets (associated with a given portfolio) is usually time-varying and asymmetric. In this paper, we conduct a simulation study to compare the discrimination ability between the well-established scores and their threshold-weighted versions with selected regions. This facilitates a comprehensive comparison of the performance of scoring rules in different settings. Our empirical applications also confirm the importance of weighted-threshold scores for accurate estimates of Value-at-risk and related measures of downside risk.

Citation

Cheng, J. (2024). Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context. Computational Economics, 64(6), 3617-3643. https://doi.org/10.1007/s10614-024-10571-y

Journal Article Type Article
Acceptance Date Feb 13, 2024
Online Publication Date Mar 16, 2024
Publication Date Dec 1, 2024
Deposit Date Apr 22, 2024
Publicly Available Date Dec 3, 2024
Journal Computational Economics
Print ISSN 0927-7099
Electronic ISSN 1572-9974
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 64
Issue 6
Pages 3617-3643
DOI https://doi.org/10.1007/s10614-024-10571-y
Keywords Weighted score, Asymmetric dependence structure, Multivariate scoring rule, Copula, Multivariate forecasting, G17, Density forecast evaluation, C53, G11
Public URL https://keele-repository.worktribe.com/output/797342
Additional Information Accepted: 13 February 2024; First Online: 16 March 2024; : ; : The author has no competing interests to declare that are relevant to the content of this article.

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Copyright Statement
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.





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