{"title":"一种具有制度转移的金融时间序列数据建模方法","authors":"Daisuke Yokouchi, Takeshi Kato, Y. Aoki","doi":"10.15057/30973","DOIUrl":null,"url":null,"abstract":"This paper proposes a new method to analyze time series data with regime shifts and makes the following three contributions: (1) it suggests an exponential weighted estimation algorithm for autoregressive model with time varying coefficients, (2) it gives a visualization technique of structural change points and an outlier measure based on the Mahalanobis distance and (3) it illustrates that our method works for hedge fund return data and high frequency FX data.","PeriodicalId":154016,"journal":{"name":"Hitotsubashi journal of commerce and management","volume":"151 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Approach to Modeling on Financial Time Series Data with Regime Shifts\",\"authors\":\"Daisuke Yokouchi, Takeshi Kato, Y. Aoki\",\"doi\":\"10.15057/30973\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new method to analyze time series data with regime shifts and makes the following three contributions: (1) it suggests an exponential weighted estimation algorithm for autoregressive model with time varying coefficients, (2) it gives a visualization technique of structural change points and an outlier measure based on the Mahalanobis distance and (3) it illustrates that our method works for hedge fund return data and high frequency FX data.\",\"PeriodicalId\":154016,\"journal\":{\"name\":\"Hitotsubashi journal of commerce and management\",\"volume\":\"151 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Hitotsubashi journal of commerce and management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15057/30973\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hitotsubashi journal of commerce and management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15057/30973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Approach to Modeling on Financial Time Series Data with Regime Shifts
This paper proposes a new method to analyze time series data with regime shifts and makes the following three contributions: (1) it suggests an exponential weighted estimation algorithm for autoregressive model with time varying coefficients, (2) it gives a visualization technique of structural change points and an outlier measure based on the Mahalanobis distance and (3) it illustrates that our method works for hedge fund return data and high frequency FX data.
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