{"title":"基于粒子滤波的GARCH过程恢复","authors":"T. Michaeli, I. Cohen","doi":"10.1109/ICASSP.2010.5495789","DOIUrl":null,"url":null,"abstract":"In this paper, we address the problem of enhancement of a noisy GARCH process using a particle filter. We compare our approach experimentally to a previously developed recursive estimation scheme. Simulations indicate that a significant gain in performance is obtained, at the cost of higher sensitivity to errors in the GARCH parameters. The proposed method allows tackling arbitrary driving noise distributions as well as arbitrary fidelity criteria.","PeriodicalId":293333,"journal":{"name":"2010 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Particle filtering based recovery of noisy GARCH processes\",\"authors\":\"T. Michaeli, I. Cohen\",\"doi\":\"10.1109/ICASSP.2010.5495789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we address the problem of enhancement of a noisy GARCH process using a particle filter. We compare our approach experimentally to a previously developed recursive estimation scheme. Simulations indicate that a significant gain in performance is obtained, at the cost of higher sensitivity to errors in the GARCH parameters. The proposed method allows tackling arbitrary driving noise distributions as well as arbitrary fidelity criteria.\",\"PeriodicalId\":293333,\"journal\":{\"name\":\"2010 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2010.5495789\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2010.5495789","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Particle filtering based recovery of noisy GARCH processes
In this paper, we address the problem of enhancement of a noisy GARCH process using a particle filter. We compare our approach experimentally to a previously developed recursive estimation scheme. Simulations indicate that a significant gain in performance is obtained, at the cost of higher sensitivity to errors in the GARCH parameters. The proposed method allows tackling arbitrary driving noise distributions as well as arbitrary fidelity criteria.
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