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A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions

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  • Hurn, A.S.
  • Lindsay, K.A.
  • McClelland, A.J.

Abstract

A quasi-maximum likelihood procedure for estimating the parameters of multi-dimensional diffusions is developed in which the transitional density is a multivariate Gaussian density with first and second moments approximating the true moments of the unknown density. For affine drift and diffusion functions, the moments are exactly those of the true transitional density and for nonlinear drift and diffusion functions the approximation is extremely good and is as effective as alternative methods based on likelihood approximations. The estimation procedure generalises to models with latent factors. A conditioning procedure is developed that allows parameter estimation in the absence of proxies.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 172 (2013)
Issue (Month): 1 ()
Pages: 106-126

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Handle: RePEc:eee:econom:v:172:y:2013:i:1:p:106-126

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Stochastic differential equations; Parameter estimation; Quasi-maximum likelihood; Moments;

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Cited by:
  1. Matyas Barczy & Gyula Pap & Tamas T. Szabo, 2014. "Parameter estimation for subcritical Heston models based on discrete time observations," Papers 1403.0527, arXiv.org.

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