Systemic Operational Risk: Does it Exist and, If So, How Do We Regulate It?

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE
P. Mcconnell, K. Blacker
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引用次数: 25

Abstract

Since the global financial crisis, banking regulators and academics have extended the traditional, narrow definition of "systemic risk" to encompass concepts such as "interconnectedness" and "shadow banking". But, at the time of writing, a definition of systemic risk that covers all of the factors that precipitated the global financial crisis is still emerging. This paper first describes the debate around the emerging definition(s) of systemic risk and discusses some of the initiatives to address systemic risk by international regulators. These initiatives include microprudential regulations, such as increasing capital for systemically important banks, and macroprudential initiatives, such as the creation of the European Systemic Risk Board. Recognizing that systemic risks arise not only from credit and market risk factors, this paper views systemic risk through the lens of operational risk, arguing that key risk factors, especially people and process risks, were pervasive across the global financial industry prior to the global financial crisis and, consequently, operational risk must be considered as a contributor to, and in some instances a trigger for, systemic risk. The paper goes on to describe the microprudential approach to operational risk within the Basel II regulations and identifies and describes operational risks that were present prior to the global financial crisis. The paper concludes that there is indeed a systemic dimension to operational risk that should be recognized and addressed by banking regulators.Finally, the paper makes some suggestions as to how the management of systemic operational risks may be addressed by banks and regulators.
系统性操作风险:是否存在?如果存在,我们如何监管?
自全球金融危机爆发以来,银行业监管机构和学术界扩大了“系统性风险”的传统狭隘定义,将“互联性”和“影子银行”等概念纳入其中。但是,在撰写本文时,一个涵盖促成全球金融危机的所有因素的系统性风险的定义仍在形成。本文首先描述了围绕系统风险的新兴定义的争论,并讨论了国际监管机构解决系统风险的一些举措。这些举措包括微观审慎监管,例如为具有系统重要性的银行增加资本,以及宏观审慎举措,例如创建欧洲系统性风险委员会。认识到系统风险不仅来自信贷和市场风险因素,本文通过操作风险的视角来看待系统风险,认为关键风险因素,特别是人员和流程风险,在全球金融危机之前遍布全球金融业,因此,操作风险必须被视为系统风险的贡献者,在某些情况下是触发系统风险的因素。本文接着描述了在巴塞尔协议II规定中对操作风险的微观审慎方法,并识别和描述了在全球金融危机之前存在的操作风险。本文的结论是,操作风险确实存在系统性维度,银行监管机构应该认识到并解决这一问题。最后,本文对银行和监管机构如何应对系统性操作风险管理提出了一些建议。
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
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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