Jie Cheng j.cheng@keele.ac.uk
Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context
Cheng, Jie
Authors
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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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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