{"title":"基于清单的加权模糊严重程度法计算巴塞尔协议II下外汇交易操作风险","authors":"V. Sree Hari Rao, K. Ramesh","doi":"10.21314/jop.2014.136","DOIUrl":null,"url":null,"abstract":"It is well-known that any risk management activity is a cost to the organization. However, optimized risk management practices satisfy regulatory capital requirements and gain the confidence of investors who take calculated risks. A bank’s risk management division will generate a profit if it can develop methodologies to decrease the nonworking regulatory capital. This may be achieved only when the risks are measured using data from internal and external sources in conjunction with scenario analysis. One such method of measuring operational risk (OR) is the advanced measurement approach. This involves quantifying ORs across the various nodes within a bank following the loss distribution approach, in which the frequency and severity distributions of the loss-generating OR events are estimated from the data sources. These distributions are then used to generate the scenarios for frequency and its associated severity for estimating the OR capital. In our approach, the various levels of loss severity are mapped to a percentage of total trade exposure, and the occurrence frequency of an OR event is assumed to follow a binomial distribution.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"15 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2014-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Checklist-Based Weighted Fuzzy Severity Approach for Calculating Operational Risk Exposure on Foreign Exchange Trades Under the Basel II Regime\",\"authors\":\"V. Sree Hari Rao, K. Ramesh\",\"doi\":\"10.21314/jop.2014.136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is well-known that any risk management activity is a cost to the organization. However, optimized risk management practices satisfy regulatory capital requirements and gain the confidence of investors who take calculated risks. A bank’s risk management division will generate a profit if it can develop methodologies to decrease the nonworking regulatory capital. This may be achieved only when the risks are measured using data from internal and external sources in conjunction with scenario analysis. One such method of measuring operational risk (OR) is the advanced measurement approach. This involves quantifying ORs across the various nodes within a bank following the loss distribution approach, in which the frequency and severity distributions of the loss-generating OR events are estimated from the data sources. These distributions are then used to generate the scenarios for frequency and its associated severity for estimating the OR capital. In our approach, the various levels of loss severity are mapped to a percentage of total trade exposure, and the occurrence frequency of an OR event is assumed to follow a binomial distribution.\",\"PeriodicalId\":54030,\"journal\":{\"name\":\"Journal of Operational Risk\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2014-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Operational Risk\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.21314/jop.2014.136\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operational Risk","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.21314/jop.2014.136","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
A Checklist-Based Weighted Fuzzy Severity Approach for Calculating Operational Risk Exposure on Foreign Exchange Trades Under the Basel II Regime
It is well-known that any risk management activity is a cost to the organization. However, optimized risk management practices satisfy regulatory capital requirements and gain the confidence of investors who take calculated risks. A bank’s risk management division will generate a profit if it can develop methodologies to decrease the nonworking regulatory capital. This may be achieved only when the risks are measured using data from internal and external sources in conjunction with scenario analysis. One such method of measuring operational risk (OR) is the advanced measurement approach. This involves quantifying ORs across the various nodes within a bank following the loss distribution approach, in which the frequency and severity distributions of the loss-generating OR events are estimated from the data sources. These distributions are then used to generate the scenarios for frequency and its associated severity for estimating the OR capital. In our approach, the various levels of loss severity are mapped to a percentage of total trade exposure, and the occurrence frequency of an OR event is assumed to follow a binomial distribution.
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.