{"title":"层次PCA与资产相关性建模","authors":"J. A. Serur, M. Avellaneda","doi":"10.2139/ssrn.3903460","DOIUrl":null,"url":null,"abstract":"Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm for identifying of homogeneous clusters of stocks, or \"synthetic sectors\". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Hierarchical PCA and Modeling Asset Correlations\",\"authors\":\"J. A. Serur, M. Avellaneda\",\"doi\":\"10.2139/ssrn.3903460\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm for identifying of homogeneous clusters of stocks, or \\\"synthetic sectors\\\". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.\",\"PeriodicalId\":108284,\"journal\":{\"name\":\"Econometric Modeling: International Financial Markets - Emerging Markets eJournal\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometric Modeling: International Financial Markets - Emerging Markets eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3903460\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3903460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm for identifying of homogeneous clusters of stocks, or "synthetic sectors". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.
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