{"title":"Scod模型:用半参数Copula方法分析工期","authors":"C. Savu, W. Ng","doi":"10.1111/j.1468-2443.2006.00051.x","DOIUrl":null,"url":null,"abstract":"This paper applies a new methodology for modeling order durations of ultra-high-frequency data using copulas. While the class of common Autoregressive Conditional Duration models are characterized by strict parameterizations and high computational burden, the semiparametric copula approach proposed here offers more flexibility in modeling the dynamic duration process by separating the marginal distributions of waiting times from their temporal dependence structure. Comparing both frameworks as to their density forecast abilities, the Semiparametric Copula Duration model clearly shows a better performance.","PeriodicalId":326622,"journal":{"name":"Wiley-Blackwell: International Review of Finance","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"The Scod Model: Analyzing Durations with a Semiparametric Copula Approach\",\"authors\":\"C. Savu, W. Ng\",\"doi\":\"10.1111/j.1468-2443.2006.00051.x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper applies a new methodology for modeling order durations of ultra-high-frequency data using copulas. While the class of common Autoregressive Conditional Duration models are characterized by strict parameterizations and high computational burden, the semiparametric copula approach proposed here offers more flexibility in modeling the dynamic duration process by separating the marginal distributions of waiting times from their temporal dependence structure. Comparing both frameworks as to their density forecast abilities, the Semiparametric Copula Duration model clearly shows a better performance.\",\"PeriodicalId\":326622,\"journal\":{\"name\":\"Wiley-Blackwell: International Review of Finance\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wiley-Blackwell: International Review of Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/j.1468-2443.2006.00051.x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley-Blackwell: International Review of Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/j.1468-2443.2006.00051.x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Scod Model: Analyzing Durations with a Semiparametric Copula Approach
This paper applies a new methodology for modeling order durations of ultra-high-frequency data using copulas. While the class of common Autoregressive Conditional Duration models are characterized by strict parameterizations and high computational burden, the semiparametric copula approach proposed here offers more flexibility in modeling the dynamic duration process by separating the marginal distributions of waiting times from their temporal dependence structure. Comparing both frameworks as to their density forecast abilities, the Semiparametric Copula Duration model clearly shows a better performance.