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

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-03-16 DOI:10.1007/s10614-024-10571-y
Jie Cheng
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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.

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在风险管理背景下使用加权多变量得分评估密度预测
评分规则通常用于评估单变量和多变量情况下密度预测的准确性。在金融风险管理中,我们主要关注密度的一个特定区域:投资组合收益分布的(左)尾部。不同资产(与给定投资组合相关)收益之间的依赖结构通常是时变和非对称的。在本文中,我们进行了一项模拟研究,以比较既定评分及其阈值加权版本与选定区域的区分能力。这有助于全面比较评分规则在不同环境下的表现。我们的实证应用也证实了加权阈值评分对于准确估算风险价值和相关下行风险度量的重要性。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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