Catalina Bolancé, Montserrat Guillén, J. Gustafsson, J. Nielsen
Our approach is based on the study of the statistical severity distribution of a single loss. We analyze the fundamental issues that arise in practice when modeling operational risk data. We address the statistical problem of estimating an operational risk distribution, both abundant data situations and when our available data is challenged from the inclusion of external data or because of underreporting. Our presentation includes an application to show that failure to account for underreporting may lead to a substantial underestimation of operational risk measures. The use of external data information can easily be incorporated in our modeling approach. The paper builds on methodology developed in Bolance et al. (2012b). 1. Quantifying Operational Risk Guided by Prior Knowledge Operational risk is one of the risks that are incorporated in the Basel II regulatory framework for financial institutions and in the Solvency II regulatory framework for insurance companies (Gatzert and Wesker, 2012 and Ashby, 2011), hence the importance of the modelization and quantification of this risk. Also, operational risk is important in the context of Enterprise Risk Management (Hoyt and Liebenberg, 2011 and Dhaene et al. 2012). One major issue addressed in Bolance et al (2012b) is how to incorporate prior knowledge into operational risk models. Such prior knowledge can come in many disguises. One being prior knowledge of parametric shapes of distributions, another being prior knowledge of the frequency of underreporting and a third could be prior knowledge arising from external data sources. The fundamental principles of mixing internal and external operational risk data was originally published in this journal in Gustafsson and Nielsen (2008) and Guillen et al. (2008). Bolance et al. (2012b) take these originally ideas and put them into a broader context, see also the following recent papers proposing alternative methods to quantify operational risk (Cope, E.W., 2012, Cavallo et al., 2012, Feng et al., 2012 and Horbenko et al., 2011). In this paper we show, with a simple example, the effect of incorporating two different types of prior knowledge into the calculation of Value-at-Risk (VaR) and Tail Value-at Risk (TVaR): external operational risk data and expert information about underreporting probability. We 1 We thank the Spanish Ministry of Science / FEDER grant ECO2010-21787-C0301 and Generalitat de Catalunya SGR 1328. Corresponding author: jens.nielsen.1@city.ac.uk 2 We thank the Spanish Ministry of Science / FEDER grant ECO2010-21787-C0301 and Generalitat de Catalunya SGR 1328. Corresponding author: jens.nielsen.1@city.ac.uk
我们的方法是基于对单个损失的统计严重性分布的研究。我们分析了操作风险数据建模在实践中出现的基本问题。我们解决了估算操作风险分布的统计问题，无论是在数据丰富的情况下，还是在我们的可用数据受到外部数据的挑战或由于漏报的情况下。我们的演示包括一个应用程序，以表明未能解释漏报可能导致对操作风险措施的严重低估。外部数据信息的使用可以很容易地结合到我们的建模方法中。本文以Bolance等人(2012b)开发的方法为基础。1. 操作风险是纳入金融机构巴塞尔协议II监管框架和保险公司偿付能力II监管框架的风险之一(Gatzert和Wesker, 2012和Ashby, 2011)，因此对该风险进行建模和量化的重要性。此外，操作风险在企业风险管理的背景下也很重要(Hoyt和Liebenberg, 2011和Dhaene et al. 2012)。Bolance等人(2012b)解决的一个主要问题是如何将先验知识纳入操作风险模型。这种先验知识可以以多种形式出现。一个是分布参数形状的先验知识，另一个是低报频率的先验知识，第三个可能是来自外部数据源的先验知识。混合内部和外部操作风险数据的基本原则最初发表在该杂志的Gustafsson和Nielsen(2008)和Guillen et al.(2008)。Bolance等人(2012b)采用了这些最初的想法，并将其置于更广泛的背景下，参见以下最近提出量化操作风险的替代方法的论文(Cope, e.w.， 2012, Cavallo等人，2012,Feng等人，2012和Horbenko等人，2011)。在本文中，我们通过一个简单的例子，展示了将两种不同类型的先验知识纳入风险价值(VaR)和尾部风险价值(TVaR)的计算中的效果:外部操作风险数据和关于低报概率的专家信息。我们感谢西班牙科学部/ FEDER资助ECO2010-21787-C0301和加泰罗尼亚政府资助SGR 1328。我们感谢西班牙科学部/ FEDER资助ECO2010-21787-C0301和Generalitat de Catalunya SGR 1328。通讯作者:jens.nielsen.1@city.ac.uk
{"title":"Adding prior knowledge to quantitative operational risk models","authors":"Catalina Bolancé, Montserrat Guillén, J. Gustafsson, J. Nielsen","doi":"10.21314/JOP.2013.120","DOIUrl":"https://doi.org/10.21314/JOP.2013.120","url":null,"abstract":"Our approach is based on the study of the statistical severity distribution of a single loss. We analyze the fundamental issues that arise in practice when modeling operational risk data. We address the statistical problem of estimating an operational risk distribution, both abundant data situations and when our available data is challenged from the inclusion of external data or because of underreporting. Our presentation includes an application to show that failure to account for underreporting may lead to a substantial underestimation of operational risk measures. The use of external data information can easily be incorporated in our modeling approach. The paper builds on methodology developed in Bolance et al. (2012b). 1. Quantifying Operational Risk Guided by Prior Knowledge Operational risk is one of the risks that are incorporated in the Basel II regulatory framework for financial institutions and in the Solvency II regulatory framework for insurance companies (Gatzert and Wesker, 2012 and Ashby, 2011), hence the importance of the modelization and quantification of this risk. Also, operational risk is important in the context of Enterprise Risk Management (Hoyt and Liebenberg, 2011 and Dhaene et al. 2012). One major issue addressed in Bolance et al (2012b) is how to incorporate prior knowledge into operational risk models. Such prior knowledge can come in many disguises. One being prior knowledge of parametric shapes of distributions, another being prior knowledge of the frequency of underreporting and a third could be prior knowledge arising from external data sources. The fundamental principles of mixing internal and external operational risk data was originally published in this journal in Gustafsson and Nielsen (2008) and Guillen et al. (2008). Bolance et al. (2012b) take these originally ideas and put them into a broader context, see also the following recent papers proposing alternative methods to quantify operational risk (Cope, E.W., 2012, Cavallo et al., 2012, Feng et al., 2012 and Horbenko et al., 2011). In this paper we show, with a simple example, the effect of incorporating two different types of prior knowledge into the calculation of Value-at-Risk (VaR) and Tail Value-at Risk (TVaR): external operational risk data and expert information about underreporting probability. We 1 We thank the Spanish Ministry of Science / FEDER grant ECO2010-21787-C0301 and Generalitat de Catalunya SGR 1328. Corresponding author: jens.nielsen.1@city.ac.uk 2 We thank the Spanish Ministry of Science / FEDER grant ECO2010-21787-C0301 and Generalitat de Catalunya SGR 1328. Corresponding author: jens.nielsen.1@city.ac.uk","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"24 1","pages":"17-32"},"PeriodicalIF":0.5,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90963470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adequate communication about operational risk in the business line","authors":"U. Milkau","doi":"10.21314/JOP.2013.119","DOIUrl":"https://doi.org/10.21314/JOP.2013.119","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"75 1","pages":"35-57"},"PeriodicalIF":0.5,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86003388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Systemic Operational Risk: Does it Exist and, If So, How Do We Regulate It?","authors":"P. Mcconnell, K. Blacker","doi":"10.21314/JOP.2013.118","DOIUrl":"https://doi.org/10.21314/JOP.2013.118","url":null,"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.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"55 1","pages":"59-99"},"PeriodicalIF":0.5,"publicationDate":"2013-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90145474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a new approach for estimating operational risk models under the loss distribution approach from historically observed losses. Our method is based on extreme value theory and, being Bayesian in nature, allows us to incorporate other external information about the unknown parameters by use of expert opinions via elicitation or external data sources. This additional information can play a crucial role in reducing the statistical uncertainty about both parameter and capital estimates in situations where observed data are insufficient to accurately estimate the tail behavior of the loss distribution. Challenges of and strategies for formulating suitable priors are discussed. A simulation study demonstrates the performance of the new approach.
{"title":"A Bayesian approach to extreme value estimation in operational risk modeling","authors":"S. Mittnik, Bakhodir A. Ergashev, E. Sekeris","doi":"10.21314/JOP.2013.131","DOIUrl":"https://doi.org/10.21314/JOP.2013.131","url":null,"abstract":"We propose a new approach for estimating operational risk models under the loss distribution approach from historically observed losses. Our method is based on extreme value theory and, being Bayesian in nature, allows us to incorporate other external information about the unknown parameters by use of expert opinions via elicitation or external data sources. This additional information can play a crucial role in reducing the statistical uncertainty about both parameter and capital estimates in situations where observed data are insufficient to accurately estimate the tail behavior of the loss distribution. Challenges of and strategies for formulating suitable priors are discussed. A simulation study demonstrates the performance of the new approach.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"4 1","pages":"55-81"},"PeriodicalIF":0.5,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78423058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The major sources of operational risk and the potential benefits of its management","authors":"Wael Hemrit, M. Arab","doi":"10.21314/JOP.2012.115","DOIUrl":"https://doi.org/10.21314/JOP.2012.115","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"29 1","pages":"71-92"},"PeriodicalIF":0.5,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83487583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fuzzy methods for variable selection in operational risk management","authors":"P. Cerchiello, Paolo Giudici","doi":"10.21314/JOP.2012.114","DOIUrl":"https://doi.org/10.21314/JOP.2012.114","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"11 1","pages":"25-41"},"PeriodicalIF":0.5,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88600650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling operational risk for good and bad bank loans","authors":"Dror Parnes","doi":"10.21314/JOP.2012.116","DOIUrl":"https://doi.org/10.21314/JOP.2012.116","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"407 1","pages":"43-67"},"PeriodicalIF":0.5,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76482986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Asymptotics for operational risk quantified with a spectral risk measure","authors":"Bingjun Tong, Chongfeng Wu","doi":"10.21314/JOP.2012.110","DOIUrl":"https://doi.org/10.21314/JOP.2012.110","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"84 1","pages":"91-116"},"PeriodicalIF":0.5,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79159390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the promise of robust statistics","authors":"J. Opdyke, A. Cavallo","doi":"10.21314/JOP.2012.111","DOIUrl":"https://doi.org/10.21314/JOP.2012.111","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"84 1","pages":"3-90"},"PeriodicalIF":0.5,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90303839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christmas 2009 did not bring much festive cheer to the shareholders of Australia's largest banks. On December 23rd, the so-called Four Pillars announced simultaneously that their subsidiaries in New Zealand had settled with the NZ Inland Revenue Department (IRD), in respect of long-running litigation that resulted in payments of unpaid tax and interest totaling some NZ$ 2.2 Billion. The settlement followed a finding in October 2009, in the High Court of New Zealand, in favor of the local tax authorities as regards a series of 'Structured Finance Transactions', which the IRD claimed were specifically designed to avoid paying tax in New Zealand. The transactions in dispute, which numbered only around 30 across the four banks, were, at face value, highly complex and intricate but when stripped of the 'smoke and mirrors' were little more than standard commercial loans. The profitability, or otherwise, of these disputed transactions depended very much on how profits, losses and tax were accounted for. Because of various tax treaties between New Zealand and Australia, the Australian parents of NZ banks are able, under certain circumstances, to offset operating losses against profits being repatriated from New Zealand. This, in effect, could turn a loss-making transaction into a powerful device for shielding profits from tax, for both the borrower and the lender. The Inland Revenue argued that the tax benefit was, in fact, the 'tax tail that wagged the commercial dog ' in such transactions. New Zealand courts at various levels agreed with this interpretation and unanimously found that the banks concerned were using these transactions to avoid paying tax.This paper argues that the losses to the Australian banks incurred as a result of the NZ Tax Scandal were, in most part, a result of Systemic Operational Risk, in particular, Legal Risk. Using examples from published court cases, the paper identifies some of the Legal Risks that arose using these transactions. The paper then suggests proactive approaches to Systemic Risk Management that should help detect and minimize the impact of similar scandals in future.
{"title":"Systemic Operational Risk – Smoke and Mirrors","authors":"P. Mcconnell","doi":"10.21314/JOP.2012.109","DOIUrl":"https://doi.org/10.21314/JOP.2012.109","url":null,"abstract":"Christmas 2009 did not bring much festive cheer to the shareholders of Australia's largest banks. On December 23rd, the so-called Four Pillars announced simultaneously that their subsidiaries in New Zealand had settled with the NZ Inland Revenue Department (IRD), in respect of long-running litigation that resulted in payments of unpaid tax and interest totaling some NZ$ 2.2 Billion. The settlement followed a finding in October 2009, in the High Court of New Zealand, in favor of the local tax authorities as regards a series of 'Structured Finance Transactions', which the IRD claimed were specifically designed to avoid paying tax in New Zealand. The transactions in dispute, which numbered only around 30 across the four banks, were, at face value, highly complex and intricate but when stripped of the 'smoke and mirrors' were little more than standard commercial loans. The profitability, or otherwise, of these disputed transactions depended very much on how profits, losses and tax were accounted for. Because of various tax treaties between New Zealand and Australia, the Australian parents of NZ banks are able, under certain circumstances, to offset operating losses against profits being repatriated from New Zealand. This, in effect, could turn a loss-making transaction into a powerful device for shielding profits from tax, for both the borrower and the lender. The Inland Revenue argued that the tax benefit was, in fact, the 'tax tail that wagged the commercial dog ' in such transactions. New Zealand courts at various levels agreed with this interpretation and unanimously found that the banks concerned were using these transactions to avoid paying tax.This paper argues that the losses to the Australian banks incurred as a result of the NZ Tax Scandal were, in most part, a result of Systemic Operational Risk, in particular, Legal Risk. Using examples from published court cases, the paper identifies some of the Legal Risks that arose using these transactions. The paper then suggests proactive approaches to Systemic Risk Management that should help detect and minimize the impact of similar scandals in future.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"77 1","pages":"119-164"},"PeriodicalIF":0.5,"publicationDate":"2012-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88392199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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