{"title":"Gumbel GARCH模型与股票应用","authors":"M. Mohammadpour, Fatemeh Ziaeenejad","doi":"10.51936/jmnw8190","DOIUrl":null,"url":null,"abstract":"The paper proposes a new GARCH model with Gumbel conditional distribution. Several statistical properties of the model are established, like autocorrelation function and stationarity. We consider two methods for estimating the unknown parameters of the model and investigate properties of the estimators. The performances of the estimators are checked by a simulation study. We investigate the application of the process using a real stock data.","PeriodicalId":242585,"journal":{"name":"Advances in Methodology and Statistics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gumbel GARCH model with stock application\",\"authors\":\"M. Mohammadpour, Fatemeh Ziaeenejad\",\"doi\":\"10.51936/jmnw8190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposes a new GARCH model with Gumbel conditional distribution. Several statistical properties of the model are established, like autocorrelation function and stationarity. We consider two methods for estimating the unknown parameters of the model and investigate properties of the estimators. The performances of the estimators are checked by a simulation study. We investigate the application of the process using a real stock data.\",\"PeriodicalId\":242585,\"journal\":{\"name\":\"Advances in Methodology and Statistics\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Methodology and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.51936/jmnw8190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Methodology and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51936/jmnw8190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper proposes a new GARCH model with Gumbel conditional distribution. Several statistical properties of the model are established, like autocorrelation function and stationarity. We consider two methods for estimating the unknown parameters of the model and investigate properties of the estimators. The performances of the estimators are checked by a simulation study. We investigate the application of the process using a real stock data.
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