{"title":"外面有人吗?从大价值传输系统事务数据中检测操作中断","authors":"Neville Arjania, Ronald Heijmans","doi":"10.21314/JFMI.2019.118","DOIUrl":null,"url":null,"abstract":"This paper develops a method to identify operational outages of participants in the Canadian Large Value Transfer System (LVTS). We define an operational outage as either no activity or unusually low activity. We test our algorithm against a database of outages reported by participants in order to reduce false negatives. The false positives can be reduced by excluding “outages” found by the algorithm if a participant historically has no payment in a given five-minute time interval. In addition, we can test whether participants do indeed report all their operational outages. The results show that our algorithm works best for the largest participants, as they send in payments continuously. Our method can be used by LVTS operators and overseers to identify sources of operational risks.<br>","PeriodicalId":41226,"journal":{"name":"Journal of Financial Market Infrastructures","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Is There Anybody Out There? Detecting Operational Outages from Large Value Transfer System Transaction Data\",\"authors\":\"Neville Arjania, Ronald Heijmans\",\"doi\":\"10.21314/JFMI.2019.118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper develops a method to identify operational outages of participants in the Canadian Large Value Transfer System (LVTS). We define an operational outage as either no activity or unusually low activity. We test our algorithm against a database of outages reported by participants in order to reduce false negatives. The false positives can be reduced by excluding “outages” found by the algorithm if a participant historically has no payment in a given five-minute time interval. In addition, we can test whether participants do indeed report all their operational outages. The results show that our algorithm works best for the largest participants, as they send in payments continuously. Our method can be used by LVTS operators and overseers to identify sources of operational risks.<br>\",\"PeriodicalId\":41226,\"journal\":{\"name\":\"Journal of Financial Market Infrastructures\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2020-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Financial Market Infrastructures\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21314/JFMI.2019.118\",\"RegionNum\":0,\"RegionCategory\":null,\"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 Financial Market Infrastructures","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21314/JFMI.2019.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Is There Anybody Out There? Detecting Operational Outages from Large Value Transfer System Transaction Data
This paper develops a method to identify operational outages of participants in the Canadian Large Value Transfer System (LVTS). We define an operational outage as either no activity or unusually low activity. We test our algorithm against a database of outages reported by participants in order to reduce false negatives. The false positives can be reduced by excluding “outages” found by the algorithm if a participant historically has no payment in a given five-minute time interval. In addition, we can test whether participants do indeed report all their operational outages. The results show that our algorithm works best for the largest participants, as they send in payments continuously. Our method can be used by LVTS operators and overseers to identify sources of operational risks.