{"title":"Selective Linear Segmentation for Detecting Relevant Parameter Changes*","authors":"A. Dufays, Elysée Aristide Houndetoungan, A. Coën","doi":"10.1093/JJFINEC/NBAA032","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA032","url":null,"abstract":"\u0000 Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44223336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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