Selective Linear Segmentation for Detecting Relevant Parameter Changes.
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...
| Publicado en: | Journal of Financial Econometrics Vol. 20; no. 4; pp. 762 - 806 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Fall2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=158655732&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 158655732 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: Fall2022 vid: 20 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 158655732 10.1093/jjfinec/nbaa032 ppf: 762 ppct: 44 formats: tig: atl: Selective Linear Segmentation for Detecting Relevant Parameter Changes. aug: au: Dufays, Arnaud Houndetoungan, Elysee Aristide Coën, Alain affil: Université Namur Université Laval CeReFiM, NaXys and CRREPl UQAM su: Monte Carlo method Hedge funds Expectation-maximization algorithms Simulated annealing Time series analysis Accounting methods sug: subj: Open-End Investment Funds Monte Carlo method Hedge funds Expectation-maximization algorithms Simulated annealing Time series analysis Accounting methods ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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