Stochastic volatility with leverage: Fast and efficient likelihood inference.
This paper is concerned with the Bayesian analysis of stochastic volatility (SV) models with leverage. Specifically, the paper shows how the often used Kim et al. [1998. Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65, 361-393] method that w...
| Publicado en: | Journal of Econometrics Vol. 140; no. 2; pp. 425 - 450 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier Science
October 2007
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| Materias: | |
| 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=511354965&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511354965 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: October 2007 vid: 140 iid: 2 pid: 1004 pub: Elsevier Science artinfo: ui: 511354965 10.1016/j.jeconom.2006.07.008 ppf: 425 ppct: 25 formats: tig: atl: Stochastic volatility with leverage: Fast and efficient likelihood inference. aug: au: Omori, Yasuhiro Chib, Siddhartha Shephard, Neil Nakajima, Jouchi su: Probability theory Stochastic processes Market volatility sug: subj: Probability theory Stochastic processes Market volatility ab: This paper is concerned with the Bayesian analysis of stochastic volatility (SV) models with leverage. Specifically, the paper shows how the often used Kim et al. [1998. Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65, 361-393] method that was developed for SV models without leverage can be extended to models with leverage. The approach relies on the novel idea of approximating the joint distribution of the outcome and volatility innovations by a suitably constructed ten-component mixture of bivariate normal distributions. The resulting posterior distribution is summarized by MCMC methods and the small approximation error in working with the mixture approximation is corrected by a reweighting procedure. The overall procedure is fast and highly efficient. We illustrate the ideas on daily returns of the Tokyo Stock Price Index. Finally, extensions of the method are described for superposition models (where the log-volatility is made up of a linear combination of heterogenous and independent autoregressions) and heavy-tailed error distributions (student and log-normal). Copyright (c) 2007 Elsevier B.V. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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