A Bayesian approach to extreme value estimation in operational risk modeling

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2013-01-01 DOI:10.21314/JOP.2013.131

S. Mittnik, Bakhodir A. Ergashev, E. Sekeris

引用次数: 9

Abstract

We propose a new approach for estimating operational risk models under the loss distribution approach from historically observed losses. Our method is based on extreme value theory and, being Bayesian in nature, allows us to incorporate other external information about the unknown parameters by use of expert opinions via elicitation or external data sources. This additional information can play a crucial role in reducing the statistical uncertainty about both parameter and capital estimates in situations where observed data are insufficient to accurately estimate the tail behavior of the loss distribution. Challenges of and strategies for formulating suitable priors are discussed. A simulation study demonstrates the performance of the new approach.

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操作风险建模中极值估计的贝叶斯方法

我们提出了一种新的方法来估计操作风险模型下的损失分布方法从历史上观察到的损失。我们的方法基于极值理论，本质上是贝叶斯理论，允许我们通过启发或外部数据源使用专家意见来合并关于未知参数的其他外部信息。在观察到的数据不足以准确估计损失分布的尾部行为的情况下，这些额外的信息可以在减少参数和资本估计的统计不确定性方面发挥关键作用。讨论了制定合适先验的挑战和策略。仿真研究验证了该方法的有效性。

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

Journal of Operational Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

40.00%

发文量

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.