Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data.
This article proposes a new approach to exploit the information in high-frequency data for the statistical inference of continuous-time affine jump diffusion (AJD) models with latent variables. For this purpose, we construct unbiased estimators of the latent variables and their power functions on th...
| Publicado en: | Journal of Financial Econometrics Vol. 5; no. 1; pp. 1 - 31 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / UK
Winter 2007
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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=511378151&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511378151 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: Winter 2007 vid: 5 iid: 1 pid: 622 pub: Oxford University Press / UK artinfo: ui: 511378151 10.1093/jjfinec/nbl007 ppf: 1 ppct: 30 formats: tig: atl: Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data. aug: au: Jiang, George J. Oomen, Roel C. A. su: Diffusion processes Estimation theory Latent variables Moments method (Statistics) sug: subj: Diffusion processes Estimation theory Latent variables Moments method (Statistics) ab: This article proposes a new approach to exploit the information in high-frequency data for the statistical inference of continuous-time affine jump diffusion (AJD) models with latent variables. For this purpose, we construct unbiased estimators of the latent variables and their power functions on the basis of the observed state variables over extended horizons. With the estimates of the latent variables, we propose a generalized method of moments (GMM) procedure for the estimation of AJD models with the distinguishing feature that moments of both observed and latent state variables can be used without resorting to path simulation or discretization of the continuous-time process. Using high frequency return observations of the S & P 500 index, we implement our estimation approach to various continuous-time asset return models with stochastic volatility and random jumps. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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