{"title":"基于gpu的最小二乘蒙特卡罗美式期权定价","authors":"M. Fatica, E. Phillips","doi":"10.1145/2535557.2535564","DOIUrl":null,"url":null,"abstract":"This paper presents an implementation of the Least Squares Monte Carlo (LSMC) method by Longstaff and Schwartz [1] to price American options on GPU using CUDA. We focused our attention to the calibration phase and performed several experiments to assess the quality of the results. The implementation can price a put option with 200,000 paths and 50 time steps in less than 10 ms on a Tesla K20X.","PeriodicalId":241950,"journal":{"name":"High Performance Computational Finance","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Pricing American options with least squares Monte Carlo on GPUs\",\"authors\":\"M. Fatica, E. Phillips\",\"doi\":\"10.1145/2535557.2535564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an implementation of the Least Squares Monte Carlo (LSMC) method by Longstaff and Schwartz [1] to price American options on GPU using CUDA. We focused our attention to the calibration phase and performed several experiments to assess the quality of the results. The implementation can price a put option with 200,000 paths and 50 time steps in less than 10 ms on a Tesla K20X.\",\"PeriodicalId\":241950,\"journal\":{\"name\":\"High Performance Computational Finance\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"High Performance Computational Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2535557.2535564\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"High Performance Computational Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2535557.2535564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pricing American options with least squares Monte Carlo on GPUs
This paper presents an implementation of the Least Squares Monte Carlo (LSMC) method by Longstaff and Schwartz [1] to price American options on GPU using CUDA. We focused our attention to the calibration phase and performed several experiments to assess the quality of the results. The implementation can price a put option with 200,000 paths and 50 time steps in less than 10 ms on a Tesla K20X.
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