{"title":"电力现货价格模型随机风险溢价的滤波与识别","authors":"S. Aihara, A. Bagchi, E. Imreizeeq","doi":"10.3182/20140824-6-ZA-1003.00325","DOIUrl":null,"url":null,"abstract":"Starting from the simple model for the spot price which is set as the jump augmented Vasicek model, we construct a factor model of the electricity futures as the stochastic hyperbolic systems with jumps. Representing the main spike phenomena of the electricity spot price from one observed futures data by proxy, the filtering of factor process and the related stochastic risk premium are formulated in a Gaussian frame work. After serving the likelihood functional, the systems parameter estimation problem is solved.","PeriodicalId":13260,"journal":{"name":"IFAC Proceedings Volumes","volume":"47 1","pages":"9563-9568"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Filtering and Identification of Stochastic Risk Premium for Electricity Spot Price Models\",\"authors\":\"S. Aihara, A. Bagchi, E. Imreizeeq\",\"doi\":\"10.3182/20140824-6-ZA-1003.00325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Starting from the simple model for the spot price which is set as the jump augmented Vasicek model, we construct a factor model of the electricity futures as the stochastic hyperbolic systems with jumps. Representing the main spike phenomena of the electricity spot price from one observed futures data by proxy, the filtering of factor process and the related stochastic risk premium are formulated in a Gaussian frame work. After serving the likelihood functional, the systems parameter estimation problem is solved.\",\"PeriodicalId\":13260,\"journal\":{\"name\":\"IFAC Proceedings Volumes\",\"volume\":\"47 1\",\"pages\":\"9563-9568\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IFAC Proceedings Volumes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3182/20140824-6-ZA-1003.00325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IFAC Proceedings Volumes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3182/20140824-6-ZA-1003.00325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Filtering and Identification of Stochastic Risk Premium for Electricity Spot Price Models
Starting from the simple model for the spot price which is set as the jump augmented Vasicek model, we construct a factor model of the electricity futures as the stochastic hyperbolic systems with jumps. Representing the main spike phenomena of the electricity spot price from one observed futures data by proxy, the filtering of factor process and the related stochastic risk premium are formulated in a Gaussian frame work. After serving the likelihood functional, the systems parameter estimation problem is solved.
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