Optimal B-Robust Posterior Distributions for Operational Risk

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2016-04-30 DOI:10.21314/jop.2016.182

Ivan Luciano Danesi, Fabio Piacenza, E. Ruli, L. Ventura

引用次数: 6

Abstract

One of the aims of operational risk modelling is to generate sound and reliable quantifications of the risk exposure, including a level of volatility that is consistent with the changes of the risk profile. One way for assuring this is by means of robust procedures, such as Optimal B-Robust estimating equations. In banking practice more than one dataset should be incorporated in the risk modelling and a coherent way to proceed to such a data integration is via Bayesian procedures. However, Bayesian inference via estimating equations in general is problematic since the likelihood function is not available. We illustrate that this issue can be dealt with using approximate Bayesian computation methods with the robust estimating function as a summary of the data. The method is illustrated by a real dataset.

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操作风险的最优b稳健后验分布

操作风险建模的目标之一是对风险敞口进行合理可靠的量化，包括与风险概况变化相一致的波动水平。保证这一点的一种方法是通过鲁棒程序，如最优b -鲁棒估计方程。在银行业实践中，风险建模中应该包含多个数据集，而进行这种数据整合的连贯方法是通过贝叶斯程序。然而，由于似然函数不可用，通常通过估计方程进行贝叶斯推理是有问题的。我们说明了这个问题可以用近似贝叶斯计算方法来处理，并以鲁棒估计函数作为数据的摘要。该方法以一个实际数据集为例进行了说明。

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.