J. Hull, Jay Cao, Jacky Chen, Zissis Poulos, dorothy zhang
{"title":"合成数据:一种新的监管工具","authors":"J. Hull, Jay Cao, Jacky Chen, Zissis Poulos, dorothy zhang","doi":"10.2139/ssrn.3908626","DOIUrl":null,"url":null,"abstract":"Machine learning tools have been developed to generate synthetic data sets that are indistinguishable from available historical data. In this paper, we investigate whether the tools can be used for stress testing. In particular we test whether synthetic data can be used to provide reliable risk measures when the confidence levels are high. Our results are encouraging and suggest that synthetic data produced from the most recent 250 days of historical data are potentially useful for determining regulatory market risk capital requirements.","PeriodicalId":152939,"journal":{"name":"Machine Learning eJournal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Synthetic Data: A New Regulatory Tool\",\"authors\":\"J. Hull, Jay Cao, Jacky Chen, Zissis Poulos, dorothy zhang\",\"doi\":\"10.2139/ssrn.3908626\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning tools have been developed to generate synthetic data sets that are indistinguishable from available historical data. In this paper, we investigate whether the tools can be used for stress testing. In particular we test whether synthetic data can be used to provide reliable risk measures when the confidence levels are high. Our results are encouraging and suggest that synthetic data produced from the most recent 250 days of historical data are potentially useful for determining regulatory market risk capital requirements.\",\"PeriodicalId\":152939,\"journal\":{\"name\":\"Machine Learning eJournal\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Learning eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3908626\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3908626","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning tools have been developed to generate synthetic data sets that are indistinguishable from available historical data. In this paper, we investigate whether the tools can be used for stress testing. In particular we test whether synthetic data can be used to provide reliable risk measures when the confidence levels are high. Our results are encouraging and suggest that synthetic data produced from the most recent 250 days of historical data are potentially useful for determining regulatory market risk capital requirements.
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