Modeling multivariate operational losses via copula-based distributions with g-and-h marginals

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2022-01-01 DOI:10.21314/jop.2021.016

M. Bee, J. Hambuckers

引用次数: 1

Abstract

We propose a family of copula-based multivariate distributions with g-and- h marginals. After studying the properties of the distribution, we develop a two-step estimation strategy and analyze via simulation the sampling distribution of the estimators. The methodology is used for the analysis of a 7-dimensional dataset containing 40,871 operational losses. The empirical evidence suggests that a distribution based on a single copula is not flexible enough, thus we model the dependence structure by means of vine copulas. We show that the approach based on regular vines improves the fit. Moreover, even though losses corresponding to different event types are found to be dependent, the assumption of perfect positive dependence is not supported by our analysis. As a result, the Value-at-Risk of the total operational loss distribution obtained from the copula- based technique is substantially smaller at high confidence levels, with respect to the one obtained using the common practice of summing the univariate Value-at-Risks.

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通过基于copula的g和h边际分布建模多变量操作损失

我们提出了一类具有g和- h边际的基于copuls的多元分布。在研究了分布的性质之后，我们提出了一种两步估计策略，并通过仿真分析了估计量的抽样分布。该方法用于分析包含40,871个操作损失的7维数据集。经验证据表明，基于单一联结的分布不够灵活，因此我们使用藤联结来建模依赖结构。我们证明了基于规则藤蔓的方法可以改善拟合。此外，即使发现不同事件类型对应的损失是依赖的，但我们的分析并不支持完全正依赖的假设。因此，相对于使用单变量风险值求和的常见做法获得的风险值，基于copula的技术获得的总运营损失分布的风险值在高置信度下要小得多。

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.