Credible value-at-risk

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE Journal of Operational Risk Pub Date : 2023-01-01 DOI:10.21314/jop.2023.005
Peter Mitic
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引用次数: 1

Abstract

Some value-at-risk (VaR) calculations yield extremely large results, which are often rejected on the grounds that they are inconsistent with the operational loss profile of the organization concerned. Therefore, an informal limit has effectively been placed on VaR. Hitherto, the concept of a “maximum” VaR has rarely been considered. In this paper, we propose an objective and simple process to determine whether or not a calculated VaR is “too large”, and thereby give a precise definition of “too large” in this context. A simple decision process, using a constant multiplier of the annualized sum of losses, is proposed to reject distributions that produce extremely high VaR values. This decision process works in conjunction with a bootstrap to also reject distributions that produce very low VaR values. Together, they determine whether or not a calculated VaR value is “credible”. A practical guide to using the combined procedures is given, along with a discussion of potential problems and viable solutions to those problems.
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可靠的风险价值
一些风险价值(VaR)计算产生了非常大的结果,这些结果常常被拒绝,理由是它们与有关组织的业务损失情况不一致。因此,对风险价值有效地设定了一个非正式的限制。迄今为止,很少考虑“最大”风险价值的概念。在本文中,我们提出了一个客观和简单的过程来确定计算的VaR是否“太大”,从而给出了在这种情况下“太大”的精确定义。本文提出了一个简单的决策过程,即使用年化损失总和的常数乘数来拒绝产生极高VaR值的分布。这个决策过程与bootstrap一起工作,也拒绝产生非常低VaR值的分布。它们共同决定了计算出的VaR值是否“可信”。给出了使用组合程序的实用指南,以及对这些问题的潜在问题和可行解决方案的讨论。
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来源期刊
Journal of Operational Risk
Journal of Operational Risk BUSINESS, FINANCE-
CiteScore
1.00
自引率
40.00%
发文量
6
期刊介绍: In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.
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