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Efficient Estimation of Jump Diffusions and General Dynamic Models with a Continuum of Moment Conditions

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  • Carrasco, Marine
  • Chernov, Mikhaël
  • Florens, Jean-Pierre
  • Ghysels, Eric

Abstract

A general estimation approach combining the attractive features of method of moments with the efficiency of ML is proposed. The moment conditions are computed via the characteristic function. The two major difficulties with the implementation is that one needs to use an infinite set of moment conditions leading to the singularity of the covariance matrix in the GMM context, and the optimal instrument yielding the ML efficiency was previously shown to depend on the unknown probability density function. We resolve the two problems simultaneously in the framework of C-GMM (GMM with a continuum of moment conditions). First, we prove asymptotic properties of the C-GMM estimator applied to dependent data and then provide a reformulation of the estimator that enhances its computational ease. Second, we propose to span the unknown optimal instrument by an infinite basis consisting of simple exponential functions. Since the estimation framework already relies on a continuum of moment conditions, adding a continuum of spanning functions does not pose any problems. As a result, we achieve ML efficiency when we use the values of conditional CF indexed by its argument as moment functions. We also introduce HAC-type estimators so that the estimation methods are not restricted to settings involving martingale difference sequences. Hence, our methods apply to Markovian and nail-Markovian dynamic models. Finally, a simulated method of moments type estimator is proposed to deal with the cases where the characteristic function does not have a closed-form expression. Extensive Monte-Carlo study based on the models typically used in term-structure literature favorably documents the performance of our methodology. L'estimation des processus de diffusion (affine ou à sauts) est problématique car l'expression de la vraisemblance n'est pas disponible. D'un autre côté, la fonction caractéristique de ces modèles est souvent connue. Cet article propose un estimateur du type méthode des

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Bibliographic Info

Paper provided by Institut d'Économie Industrielle (IDEI), Toulouse in its series IDEI Working Papers with number 116.

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Date of creation: 2000
Date of revision: 2002
Handle: RePEc:ide:wpaper:1037

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Citations

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Cited by:
  1. Michael Sørensen & Julie Lyng Forman, 2007. "The Pearson diffusions: A class of statistically tractable diffusion processes," CREATES Research Papers 2007-28, School of Economics and Management, University of Aarhus.
  2. Garcia, René & Renault, Eric & Veredas, David, 2011. "Estimation of stable distributions by indirect inference," Journal of Econometrics, Elsevier, vol. 161(2), pages 325-337, April.
  3. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
  4. Carrasco, Marine & Florens, Jean-Pierre, 2003. "On the Asymptotic Efficiency of GMM," IDEI Working Papers 173, Institut d'Économie Industrielle (IDEI), Toulouse.
  5. Hao Zhou, 2003. "Itô Conditional Moment Generator and the Estimation of Short-Rate Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(2), pages 250-271.
  6. Kim, Myung Suk & Wang, Suojin, 2008. "Consistent estimation in regression models for the drift function in some continuous time models," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2682-2691, January.
  7. Ghysels, Eric & Tauchen, George, 2003. "Frontiers of financial econometrics and financial engineering," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 1-7.
  8. Qiang Dai & Kenneth Singleton, 2003. "Term Structure Dynamics in Theory and Reality," Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 631-678, July.
  9. Marine Carrasco, 2004. "Chi-square Tests for Parameter Stability," RCER Working Papers 508, University of Rochester - Center for Economic Research (RCER).

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