解剖摩根大通鲸鱼:一个事后解剖

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE Journal of Operational Risk Pub Date : 2014-06-30 DOI:10.21314/JOP.2014.144
P. Mcconnell
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引用次数: 15

摘要

在许多方面,摩根大通的“伦敦鲸”丑闻与其他“流氓交易”事件类似,因为一群交易员在复杂的衍生品证券中持有大量投机性头寸,这些头寸出现了问题,给该公司造成了超过60亿美元的交易损失。与其他流氓交易案件一样,人们不顾一切地试图掩盖损失,直到损失大到无法忽视,最终不得不在银行的财务账户中得到确认。然而,“鲸鱼案”(因所涉及交易头寸的庞大规模而被称为“鲸鱼案”)在几个重要方面不同于其他流氓交易案件,尤其是因为这些头寸的庞大规模和风险为摩根大通的许多高管所熟知。摩根大通以拥有先进的风险管理能力和系统而自豪。模型风险在这起丑闻中的作用,虽然不是主要原因,但很重要,因为至少部分推动巨额头寸的动力是由于不正确的风险建模。对这些事件进行的各种外部和内部调查得出的结论是,该行关键的风险管理流程出现了故障,不仅是首席投资办公室(Chief Investment Office)——损失发生的部门,整个银行都出现了故障。特别是,该公司的模型开发和批准流程存在缺陷,这使得交易员在交易时低估了他们所面临的风险。根据巴塞尔协议II的规定,由于流程失败造成的损失被归类为操作风险损失,因此这个案例表明了摩根大通操作风险管理的重大失败。本文采用已故巴里·特纳教授分析组织灾难的框架,从操作风险的角度剖析了鲸鱼丑闻。本文还就如何管理模型风险以防止未来发生类似损失提出了建议。
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Dissecting the JPMorgan Whale: A Post-Mortem
In many respects, the “London whale” scandal at JPMorgan Chase is similar to other “rogue trading” events, in that a group of traders took large, speculative positions in complex derivative securities that went wrong, resulting in over US$6 billion of trading losses to the firm. As in other rogue trading cases, there were desperate attempts to cover up the losses until they became too big to ignore and eventually had to be recognized in the financial accounts of the bank. However, the whale case, so-called because of the sheer size of the trading positions involved, differs in several important respects from other rogue trading cases, not least because the sheer size and riskiness of the positions were well-known to many executives within JPMorgan, a firm that prided itself on having advanced risk management capabilities and systems. The role of Model Risk in this scandal, while not the primary cause, is important in that at least part of the impetus to take huge positions was due to incorrect risk modeling. Various external and internal inquiries into the events have concluded that critical risk management processes in the bank broke down, not only in the Chief Investment Office, the division in which the losses occurred, but across the bank. In particular, deficiencies in the firm’s Model Development and Approval processes allowed traders to trade while underestimating the risks that they were running. Under Basel II regulations, losses due to process failure are classified as operational risk losses and hence this case demonstrates a significant failure of operational risk management in JPMorgan. This paper dissects the whale scandal from an operational risk perspective using the late Professor Barry Turner’s framework for analyzing organizational disasters. The paper also makes suggestions as to how model risk may be managed to prevent similar losses in future.
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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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