{"title":"CVA 敏感性、套期保值和风险","authors":"Stéphane CrépeyUFR Mathématiques UPCité, Botao LiLPSM, Hoang NguyenIES, LPSM, Bouazza Saadeddine","doi":"arxiv-2407.18583","DOIUrl":null,"url":null,"abstract":"We present a unified framework for computing CVA sensitivities, hedging the\nCVA, and assessing CVA risk, using probabilistic machine learning meant as\nrefined regression tools on simulated data, validatable by low-cost companion\nMonte Carlo procedures. Various notions of sensitivities are introduced and\nbenchmarked numerically. We identify the sensitivities representing the best\npractical trade-offs in downstream tasks including CVA hedging and risk\nassessment.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CVA Sensitivities, Hedging and Risk\",\"authors\":\"Stéphane CrépeyUFR Mathématiques UPCité, Botao LiLPSM, Hoang NguyenIES, LPSM, Bouazza Saadeddine\",\"doi\":\"arxiv-2407.18583\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a unified framework for computing CVA sensitivities, hedging the\\nCVA, and assessing CVA risk, using probabilistic machine learning meant as\\nrefined regression tools on simulated data, validatable by low-cost companion\\nMonte Carlo procedures. Various notions of sensitivities are introduced and\\nbenchmarked numerically. We identify the sensitivities representing the best\\npractical trade-offs in downstream tasks including CVA hedging and risk\\nassessment.\",\"PeriodicalId\":501294,\"journal\":{\"name\":\"arXiv - QuantFin - Computational Finance\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Computational Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2407.18583\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Computational Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.18583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a unified framework for computing CVA sensitivities, hedging the
CVA, and assessing CVA risk, using probabilistic machine learning meant as
refined regression tools on simulated data, validatable by low-cost companion
Monte Carlo procedures. Various notions of sensitivities are introduced and
benchmarked numerically. We identify the sensitivities representing the best
practical trade-offs in downstream tasks including CVA hedging and risk
assessment.