By using information about the ownership structure of listed companies from 2004 to 2016, we construct the cross-shareholding network for each year and examine the effects of the network position of a firm on extreme price movement. The results show that firms that are in more central positions exhibit less extreme price movements because they have more connections with other firms, because they can collect or disseminate information more easily through their connections and because their price information transparency is higher. Moreover, we examine the different effects of network structure on extreme upward and downward movements in price and find that the centrality of a firm more strongly inhibits extreme price upward movements than it does downward movements. Our results suggest that a firm’s position in the cross-shareholding network can influence its extreme price movements, which gives us new insights into extreme stock market movements and provides useful suggestions for future financial regulations.
{"title":"The Impact of the Cross-Shareholding Network on Extreme Price Movements: Evidence from China","authors":"Jie Cao, Fenghua Wen","doi":"10.21314/jor.2019.423","DOIUrl":"https://doi.org/10.21314/jor.2019.423","url":null,"abstract":"By using information about the ownership structure of listed companies from 2004 to 2016, we construct the cross-shareholding network for each year and examine the effects of the network position of a firm on extreme price movement. The results show that firms that are in more central positions exhibit less extreme price movements because they have more connections with other firms, because they can collect or disseminate information more easily through their connections and because their price information transparency is higher. Moreover, we examine the different effects of network structure on extreme upward and downward movements in price and find that the centrality of a firm more strongly inhibits extreme price upward movements than it does downward movements. Our results suggest that a firm’s position in the cross-shareholding network can influence its extreme price movements, which gives us new insights into extreme stock market movements and provides useful suggestions for future financial regulations.<br>","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"132 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89285852","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}
Ruben D. Cohen, Jonathan Humphries, S. Veau, R. Francis
Cyber risk is one of the most challenging areas of risk, not only because it is relatively nascent but also because it remains an elusive moving target due to an ever-evolving threat landscape. A lack of structured data and the systemic implications of multifaceted impacts of overlapping risk frameworks are additional factors that make this risk difficult to quantify. As a starting point for overcoming this challenge, our paper considers a potential definition of this risk type, encompassing confidentiality, integrity and availability; the key components of a cyber-risk framework; a taxonomy to help establish a common framework for data collection to aid quantification; and the key quantification challenges. It then focuses on quantifying the direct financial and compensatory losses emanating from cyber risks. To help us carry this out, dimensional analysis is incorporated in the same manner as it has been applied to operational losses; this enables the identification of any similarities and/ or gross deviations between the profiles of cyber and non-cyber operational losses. In all, considering the limited amount of cyber data available, this analysis shows that: (1) a taxonomy for cyber risk that maps directly to operational risk might be a worthwhile exercise; (2) cyber loss data has a fundamental risk profile similar to that of non-cyber operational risk losses, with both following the same trend; and (3) the underlying risk profile related to cyber losses has not changed materially over time. These findings come with the added implications that: (1) mapping the taxonomies of cyber and operational risk against each other could be conducted more objectively; (2) operational risk modeling techniques that have been developed over the past decade or so could be used in the same way to assess the direct financial impact of cyber risk as a starting point; and (3) although there has been an increase in both the frequency and the severity of cyber losses over the past few years, there has not been a major paradigm shift in their fundamental risk profile over the same period of time.
{"title":"An Investigation of Cyber Loss Data and Its Links to Operational Risk","authors":"Ruben D. Cohen, Jonathan Humphries, S. Veau, R. Francis","doi":"10.21314/jop.2019.228","DOIUrl":"https://doi.org/10.21314/jop.2019.228","url":null,"abstract":"Cyber risk is one of the most challenging areas of risk, not only because it is relatively nascent but also because it remains an elusive moving target due to an ever-evolving threat landscape. A lack of structured data and the systemic implications of multifaceted impacts of overlapping risk frameworks are additional factors that make this risk difficult to quantify. As a starting point for overcoming this challenge, our paper considers a potential definition of this risk type, encompassing confidentiality, integrity and availability; the key components of a cyber-risk framework; a taxonomy to help establish a common framework for data collection to aid quantification; and the key quantification challenges. It then focuses on quantifying the direct financial and compensatory losses emanating from cyber risks. To help us carry this out, dimensional analysis is incorporated in the same manner as it has been applied to operational losses; this enables the identification of any similarities and/ or gross deviations between the profiles of cyber and non-cyber operational losses. In all, considering the limited amount of cyber data available, this analysis shows that: \u0000 \u0000(1) a taxonomy for cyber risk that maps directly to operational risk might be a worthwhile exercise; \u0000 \u0000(2) cyber loss data has a fundamental risk profile similar to that of non-cyber operational risk losses, with both following the same trend; and \u0000 \u0000(3) the underlying risk profile related to cyber losses has not changed materially over time. \u0000 \u0000These findings come with the added implications that: \u0000 \u0000(1) mapping the taxonomies of cyber and operational risk against each other could be conducted more objectively; \u0000 \u0000(2) operational risk modeling techniques that have been developed over the past decade or so could be used in the same way to assess the direct financial impact of cyber risk as a starting point; and \u0000 \u0000(3) although there has been an increase in both the frequency and the severity of cyber losses over the past few years, there has not been a major paradigm shift in their fundamental risk profile over the same period of time.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"59 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84848057","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 problem of occupational fraud is one of the most wide-reaching operational risk event types in the Nigerian banking system. This event type spans many departments, roles, processes and systems and causes significant financial and reputational damage to banks. As a result, fraud presents banks with a real challenge in terms of knowing where to start. One of the main aims of this paper is to use stochastic probability models to predict aggregate fraud severity and fraud frequency within the Nigerian banking sector using historical data. Another objective is to describe how banks can develop and deploy business intelligence (BI) outlier-based detection models to recognize internal fraudulent activities. As the volume of transaction data grows and the industry focuses more closely on fraud detection, BI has evolved to provide proactive, real-time insights into fraudulent behaviors and activities. We discuss the fraud analytic development process, since it is a central issue in real application domains.
{"title":"The use of business intelligence and predictive analytics in detecting and managing occupational fraud in Nigerian banks","authors":"C. Nwafor, O. Nwafor, Chris Onalo","doi":"10.21314/jop.2019.227","DOIUrl":"https://doi.org/10.21314/jop.2019.227","url":null,"abstract":"The problem of occupational fraud is one of the most wide-reaching operational risk event types in the Nigerian banking system. This event type spans many departments, roles, processes and systems and causes significant financial and reputational damage to banks. As a result, fraud presents banks with a real challenge in terms of knowing where to start. One of the main aims of this paper is to use stochastic probability models to predict aggregate fraud severity and fraud frequency within the Nigerian banking sector using historical data. Another objective is to describe how banks can develop and deploy business intelligence (BI) outlier-based detection models to recognize internal fraudulent activities. As the volume of transaction data grows and the industry focuses more closely on fraud detection, BI has evolved to provide proactive, real-time insights into fraudulent behaviors and activities. We discuss the fraud analytic development process, since it is a central issue in real application domains.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"25 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86801501","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}
In the loss distribution approach (LDA), the most widely used approach of operational risk measurement, the modeling dependencies across different risk cells have been extensively studied. However, it has not been recognized that the dependencies between high-frequency, low-impact (HFLI) and low-frequency, high-impact (LFHI) operational risk losses are naturally different. This paper proposes an approach, called the loss distribution approach with segmented dependence (LDA-SD), which can model the different dependencies of HFLI and LFHI losses in the framework of LDA. LDA-SD divides the losses into two parts for HFLI and LFHI, fits their frequency and severity distributions separately and models the segmented dependencies with a copula. In our empirical study, the proposed LDA-SD is applied to measure the operational risk of the overall Chinese banking sector based on the Chinese Operational Loss Database data set, the largest operational risk data set in China. The empirical results reveal that the dependencies are indeed different between HFLI and LFHI losses. The operational risk capital calculated by the LDA-SD is significantly smaller than that calculated by the LDA and considering the holistic dependence, but larger than that simply considering tail dependence.
{"title":"Operational Risk Measurement: A Loss Distribution Approach with Segmented Dependence","authors":"Xiaoqian Zhu, Yinghui Wang, Jianping Li","doi":"10.21314/JOP.2019.220","DOIUrl":"https://doi.org/10.21314/JOP.2019.220","url":null,"abstract":"In the loss distribution approach (LDA), the most widely used approach of operational risk measurement, the modeling dependencies across different risk cells have been extensively studied. However, it has not been recognized that the dependencies between high-frequency, low-impact (HFLI) and low-frequency, high-impact (LFHI) operational risk losses are naturally different. This paper proposes an approach, called the loss distribution approach with segmented dependence (LDA-SD), which can model the different dependencies of HFLI and LFHI losses in the framework of LDA. LDA-SD divides the losses into two parts for HFLI and LFHI, fits their frequency and severity distributions separately and models the segmented dependencies with a copula. In our empirical study, the proposed LDA-SD is applied to measure the operational risk of the overall Chinese banking sector based on the Chinese Operational Loss Database data set, the largest operational risk data set in China. The empirical results reveal that the dependencies are indeed different between HFLI and LFHI losses. The operational risk capital calculated by the LDA-SD is significantly smaller than that calculated by the LDA and considering the holistic dependence, but larger than that simply considering tail dependence.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"11 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79052238","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":"A review of the state of the art in quantifying operational risk","authors":"Sonia Benito, C. Martín","doi":"10.21314/JOP.2018.214","DOIUrl":"https://doi.org/10.21314/JOP.2018.214","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"13 1","pages":"89-129"},"PeriodicalIF":0.5,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87519776","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":"Bridging networks, systems and controls frameworks for cybersecurity curriculums and standards development","authors":"Yogesh Malhotra","doi":"10.21314/jop.2018.201","DOIUrl":"https://doi.org/10.21314/jop.2018.201","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"19 1","pages":"77-99"},"PeriodicalIF":0.5,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532300","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":"Forward-looking and incentive-compatible operational risk capital framework","authors":"Marco Migueis","doi":"10.21314/jop.2018.219","DOIUrl":"https://doi.org/10.21314/jop.2018.219","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"28 12 1","pages":"1-15"},"PeriodicalIF":0.5,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532315","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}
Gareth W. Peters,George Clark,John Thirlwell,Manoj Kulwal
{"title":"Global perspectives on operational risk management and practice: a survey by the Institute of Operational Risk (IOR) and the Center for Financial Professionals (CeFPro)","authors":"Gareth W. Peters,George Clark,John Thirlwell,Manoj Kulwal","doi":"10.21314/jop.2018.215","DOIUrl":"https://doi.org/10.21314/jop.2018.215","url":null,"abstract":"","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"66 1","pages":"47-88"},"PeriodicalIF":0.5,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532305","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}
Events in the last decade (such as the financial crisis, the settlement of legal claims against banks and criminal activity in the form of rogue trading) have underlined the importance of non-financial risks for banks, ie, risks other than market or credit risk. Approaches to dealing with non-financial risk range from risk governance to the methodology of modeling risk. The behavior of decision makers has been identified as a new type of risk: behavioral risk. However, less attention is paid to behavioral risk management in the first line of defence. This paper discusses key features of fighting behavioral risk in the business line of operations as the central hub for all transactions in a bank. Starting with examples such as best practice in aviation, it will examine different possible solutions, from training single subject-matter experts to communication at enterprise level.
{"title":"Management of Behavioral Risk in the First Line of Defence","authors":"Jürgen Bott, U. Milkau","doi":"10.21314/JOP.2017.199","DOIUrl":"https://doi.org/10.21314/JOP.2017.199","url":null,"abstract":"Events in the last decade (such as the financial crisis, the settlement of legal claims against banks and criminal activity in the form of rogue trading) have underlined the importance of non-financial risks for banks, ie, risks other than market or credit risk. Approaches to dealing with non-financial risk range from risk governance to the methodology of modeling risk. The behavior of decision makers has been identified as a new type of risk: behavioral risk. However, less attention is paid to behavioral risk management in the first line of defence. This paper discusses key features of fighting behavioral risk in the business line of operations as the central hub for all transactions in a bank. Starting with examples such as best practice in aviation, it will examine different possible solutions, from training single subject-matter experts to communication at enterprise level.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"4 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2017-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81164435","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}
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.
{"title":"A Structural Model for Estimating Losses Associated with the Mis-selling of Retail Banking Products","authors":"Huan Yan, R. Wood","doi":"10.21314/JOP.2017.186","DOIUrl":"https://doi.org/10.21314/JOP.2017.186","url":null,"abstract":"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.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"33 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2017-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79023367","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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