{"title":"用COGARCH(p,q)模型测量风险","authors":"F. Bianchi, L. Mercuri, Edit Rroji","doi":"10.2139/ssrn.2852858","DOIUrl":null,"url":null,"abstract":"In this paper we introduce a multivariate Independent Component COGARCH(p,q) model for financial time series. We determine optimal portfolio weights obtained as a solution of different static asset allocation problems. Empirical analysis is conducted on two datasets. The first is composed by 154 European hedge funds tracking the performance of the FTSE100 Index while the second contains the members of FTSE100. The performances of different strategies are investigated from an out-of-sample perspective.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"81 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Measuring Risk with COGARCH(p,q) Models\",\"authors\":\"F. Bianchi, L. Mercuri, Edit Rroji\",\"doi\":\"10.2139/ssrn.2852858\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we introduce a multivariate Independent Component COGARCH(p,q) model for financial time series. We determine optimal portfolio weights obtained as a solution of different static asset allocation problems. Empirical analysis is conducted on two datasets. The first is composed by 154 European hedge funds tracking the performance of the FTSE100 Index while the second contains the members of FTSE100. The performances of different strategies are investigated from an out-of-sample perspective.\",\"PeriodicalId\":11800,\"journal\":{\"name\":\"ERN: Stock Market Risk (Topic)\",\"volume\":\"81 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Stock Market Risk (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2852858\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Stock Market Risk (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2852858","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we introduce a multivariate Independent Component COGARCH(p,q) model for financial time series. We determine optimal portfolio weights obtained as a solution of different static asset allocation problems. Empirical analysis is conducted on two datasets. The first is composed by 154 European hedge funds tracking the performance of the FTSE100 Index while the second contains the members of FTSE100. The performances of different strategies are investigated from an out-of-sample perspective.
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