{"title":"The Role of systemic people risk in the global financial crisis","authors":"P. Mcconnell, K. Blacker","doi":"10.21314/JOP.2011.095","DOIUrl":"https://doi.org/10.21314/JOP.2011.095","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"130 1","pages":"65-123"},"PeriodicalIF":0.5,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88765544","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":"Can the single-loss approximation method compete with the standard monte carlo simulation technique?","authors":"C. Hess","doi":"10.21314/JOP.2011.091","DOIUrl":"https://doi.org/10.21314/JOP.2011.091","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"12 1","pages":"31-43"},"PeriodicalIF":0.5,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84546205","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 disclosure of operational risk in tunisian insurance companies","authors":"Wael Hemrit, M. Arab","doi":"10.21314/JOP.2011.089","DOIUrl":"https://doi.org/10.21314/JOP.2011.089","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"21 1","pages":"69-111"},"PeriodicalIF":0.5,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72693336","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":"Information technology at the forefront of operational risk: banks are at a greater risk","authors":"M. Fheili","doi":"10.21314/JOP.2011.092","DOIUrl":"https://doi.org/10.21314/JOP.2011.092","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"18 1","pages":"47-67"},"PeriodicalIF":0.5,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74939174","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 impact of the financial crisis on operational risk in the financial services industry: empirical evidence","authors":"C. Hess","doi":"10.21314/JOP.2011.087","DOIUrl":"https://doi.org/10.21314/JOP.2011.087","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"34 1","pages":"23-35"},"PeriodicalIF":0.5,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78140996","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":"Accounting and risk management: the need for integration","authors":"Brendon Young","doi":"10.21314/JOP.2011.097","DOIUrl":"https://doi.org/10.21314/JOP.2011.097","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"256 1","pages":"37-53"},"PeriodicalIF":0.5,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73113519","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 most insidious operational risk: lack of effective information sharing","authors":"S. Francis","doi":"10.21314/JOP.2011.086","DOIUrl":"https://doi.org/10.21314/JOP.2011.086","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"17 1","pages":"55-68"},"PeriodicalIF":0.5,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83660899","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}
The topic of operational risk has gained increasing attention in both academic research and in practice. We discuss means to quantify operational risk with specific focus on manufacturing companies. In line with the view of depicting operations of a company using material, financial and information flows, we extend the idea of overlaying the three flows with risk flow to assess operational risk. We demonstrate the application of the risk flow concept by discussing a case study with a consumer goods company. We implemented the model using discrete-event and Monte Carlo simulation techniques. Results from the simulation are evaluated to show how specific parameter changes affect the level of operational risk exposure for this company. Introduction The number of major incidences and catastrophic events affecting global business operations is on the rise. The impact of recent volcano eruption in Iceland, earthquakes around the world, the BP oil spill and financial crisis is making headlines but companies may never know the true extent of the loss. These events reinforce the need for companies to consider operational risk in a more formal manner and act strategically to minimize the negative impact of these and other types of disruptions. Having a better view of operational risks can allow a company to act proactively in many cases to come out unscathed in fact such a capability can be converted into a competitive advantage. Quantification and measurement is an integral part of managing operational risk. The topic of operational risk is very central to the financial industry due to the immediate and very direct impact of the bankruptcy of a financial institution on the economy and businesses. Not surprisingly, therefore, it has attracted a lot of attention from regulators, academics and practitioners alike. Targeted efforts have been made in researching operational risk especially since the Basel II guidelines on its assessment and the building of capital reserves came out in 2001 [1]. But the breadth of the catastrophic disasters mentioned above raises an important question: Is the domain of operational risk measurement too narrowly focused on financial institutions and their risk exposures? Clearly, assessing operational risk exposure is necessary in non-financial companies as well. To this end, we propose a method to quantify operational risk for any organization including non-financial companies. From this point forward, we will use risk and operational risk interchangeably and discuss it in the context of a manufacturing environment. A fundamental issue in studying operational risk is a lack of uniform understanding of its meaning among academics and practitioners. Operational risk has been defined in a variety of ways in the literature so for the purpose of this research, we will adopt the definition proposed by the Basel Committee to define operational risk “as the risk of loss resulting from inadequate or failed internal processes, people and
{"title":"Operational risk quantification : a risk flow approach","authors":"G. Finke, Mahender P. Singh, S. Rachev","doi":"10.21314/JOP.2010.083","DOIUrl":"https://doi.org/10.21314/JOP.2010.083","url":null,"abstract":"The topic of operational risk has gained increasing attention in both academic research and in practice. We discuss means to quantify operational risk with specific focus on manufacturing companies. In line with the view of depicting operations of a company using material, financial and information flows, we extend the idea of overlaying the three flows with risk flow to assess operational risk. We demonstrate the application of the risk flow concept by discussing a case study with a consumer goods company. We implemented the model using discrete-event and Monte Carlo simulation techniques. Results from the simulation are evaluated to show how specific parameter changes affect the level of operational risk exposure for this company. Introduction The number of major incidences and catastrophic events affecting global business operations is on the rise. The impact of recent volcano eruption in Iceland, earthquakes around the world, the BP oil spill and financial crisis is making headlines but companies may never know the true extent of the loss. These events reinforce the need for companies to consider operational risk in a more formal manner and act strategically to minimize the negative impact of these and other types of disruptions. Having a better view of operational risks can allow a company to act proactively in many cases to come out unscathed in fact such a capability can be converted into a competitive advantage. Quantification and measurement is an integral part of managing operational risk. The topic of operational risk is very central to the financial industry due to the immediate and very direct impact of the bankruptcy of a financial institution on the economy and businesses. Not surprisingly, therefore, it has attracted a lot of attention from regulators, academics and practitioners alike. Targeted efforts have been made in researching operational risk especially since the Basel II guidelines on its assessment and the building of capital reserves came out in 2001 [1]. But the breadth of the catastrophic disasters mentioned above raises an important question: Is the domain of operational risk measurement too narrowly focused on financial institutions and their risk exposures? Clearly, assessing operational risk exposure is necessary in non-financial companies as well. To this end, we propose a method to quantify operational risk for any organization including non-financial companies. From this point forward, we will use risk and operational risk interchangeably and discuss it in the context of a manufacturing environment. A fundamental issue in studying operational risk is a lack of uniform understanding of its meaning among academics and practitioners. Operational risk has been defined in a variety of ways in the literature so for the purpose of this research, we will adopt the definition proposed by the Basel Committee to define operational risk “as the risk of loss resulting from inadequate or failed internal processes, people and ","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"24 1","pages":"65-89"},"PeriodicalIF":0.5,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85659457","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":"Recursions and Fast Fourier Transforms for Certain Bivariate Compound Distributions","authors":"Tao Jin, Jiandong Ren","doi":"10.21314/JOP.2010.085","DOIUrl":"https://doi.org/10.21314/JOP.2010.085","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"448 1","pages":"19-33"},"PeriodicalIF":0.5,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77041790","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 Loss Severity Distributions from Consortium Data","authors":"Eric Cope","doi":"10.21314/JOP.2010.082","DOIUrl":"https://doi.org/10.21314/JOP.2010.082","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"19 1","pages":"35-64"},"PeriodicalIF":0.5,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91200415","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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