一个估计零售银行产品不当销售损失的结构模型

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE Journal of Operational Risk Pub Date : 2017-02-24 DOI:10.21314/JOP.2017.186
Huan Yan, R. Wood
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引用次数: 3

摘要

在本文中,提出了一个结构模型来估计与零售银行产品不当销售相关的损失。这是第一篇考虑为这种操作/行为风险场景建立基于因素的模型的论文。所采用的方法在已建立的损失分布方法(LDA)下使用频率/严重性技术。本文不是通过典型的损失数据或专家意见来校准成分分布,而是开发了一种结构方法,在这种方法中,这些分布是使用基于潜在风险驱动因素和动态的定制模型来确定的。对于零售不当销售,使用贝叶斯网络构建频率分布,使用系统动力学构建严重程度分布。迄今为止,这还没有用于基于驾驶员的操作风险模型。在使用系统动力学,结合排队论和多目标优化的元素,本文通过确保模型适当地代表所讨论的场景,提倡在建模方面采取一种通用的态度。然后,将构建的模型应用于涉及打包银行账户的特定且当前相关的场景,并确定说明性资本估计。本文发现,在情景级风险量化中,使用结构模型可以提供比使用损失数据或专家意见更具有风险敏感性的替代方案。此外,这些模型可用于各种风险管理用途,例如评估控制效力以及业务和资源规划。
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A Structural Model for Estimating Losses Associated with the Mis-selling of Retail Banking Products
In this paper, a structural model is presented for estimating losses associated with the mis-selling of retail banking products. This is the first paper to consider factor-based modeling for this operational/conduct risk scenario. The approach employed makes use of frequency/severity techniques under the established loss distribution approach (LDA). Rather than calibrate the constituent distributions through the typical means of loss data or expert opinion, this paper develops a structural approach in which these are determined using bespoke models built on the underlying risk drivers and dynamics. For retail mis-selling, the frequency distribution is constructed using a Bayesian network, while the severity distribution is constructed using system dynamics. This has not been used to date in driver-based models for operational risk. In using system dynamics, with elements of queuing theory and multi-objective optimization, this paper advocates a versatile attitude with regard to modeling by ensuring the model is appropriately representative of the scenario in question. The constructed model is thereafter applied to a specific and currently relevant scenario involving packaged bank accounts, and illustrative capital estimates are determined. This paper finds that using structural models could provide a more risk-sensitive alternative to using loss data or expert opinion in scenario-level risk quantification. Further, these models could be exploited for a variety of risk management uses, such as the assessment of control efficacy and operational and resource planning.
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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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