Modeling Maximum Entropy Distributions for Financial Returns by Moment Combination and Selection.
In empirical finance, conditional distributions of financial returns are often established by specifying the standardized error distributions of GARCH-type models. In this article, we apply the maximum entropy (MaxEnt) approach and propose a moment combination and selection method to explore this di...
| Publicado en: | Journal of Financial Econometrics Vol. 13; no. 2; pp. 414 - 456 |
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| Formato: | Artículo |
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Oxford University Press / USA
Spring2015
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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=103148960&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 103148960 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: Spring2015 vid: 13 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 103148960 10.1093/jjfinec/nbt007 ppf: 414 ppct: 42 formats: tig: atl: Modeling Maximum Entropy Distributions for Financial Returns by Moment Combination and Selection. aug: au: YI-TING CHEN affil: Institute of Economics, Academia Sinica su: Rate of return on stocks Mathematical models Maximum entropy method Statistical errors GARCH model Distribution (Probability theory) sug: subj: Rate of return on stocks Mathematical models Maximum entropy method Statistical errors GARCH model Distribution (Probability theory) keyword: GARCH-type models maximum entropy moment combination moment selection standardized error distribution GARCH-type models maximum entropy moment combination moment selection standardized error distribution ab: In empirical finance, conditional distributions of financial returns are often established by specifying the standardized error distributions of GARCH-type models. In this article, we apply the maximum entropy (MaxEnt) approach and propose a moment combination and selection method to explore this distribution-building problem. We demonstrate that this framework is useful for unifying and comparing existing distribution specifications, generating more suitable distribution specifications, and shedding light on the roles of different moments in the distribution-building process. We also show the applicability of our method to real data by means of an empirical study on stock index returns. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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