Assessing model risk in financial and energy markets using dynamic conditional VaRs

IF 1.3 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2023-10-14 DOI:10.1002/asmb.2828

Angelica Gianfreda, Giacomo Scandolo

引用次数: 0

Abstract

It has been recognized that model risk has an important effect on any risk measurement procedures, particularly when dealing with complex markets and in the presence of a wide range of implemented models. We consider a normalized measure of model risk for the forecast of daily Value-at-Risk, combined with a model selection and an averaging procedure. This allows us to restrict the set of plausible models on a daily basis, making the initial choice of competing models less crucial and then yielding a more reliable assessment of model risk. Using AR-GARCH-type models with different distributions for the innovations, we assess the dynamics of model risk for different financial assets (a stock, an equity index, an exchange rate) and commodities (electricity, crude oil and natural gas) over 15 years.

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利用动态条件风险价值值评估金融和能源市场的模型风险

人们已经认识到，模型风险对任何风险测量程序都有重要影响，尤其是在处理复杂市场和存在各种已实施模型的情况下。我们考虑对每日风险价值预测的模型风险进行归一化衡量，并结合模型选择和平均程序。这样，我们就可以限制每天的可信模型集，使最初选择竞争模型的关键性降低，进而对模型风险进行更可靠的评估。我们使用具有不同创新分布的 AR-GARCH 型模型，对 15 年来不同金融资产（股票、股票指数、汇率）和商品（电力、原油和天然气）的模型风险动态进行了评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.