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Kernel Autocorrelogram for Time Deformed Processes

Listed author(s):
  • Eric Ghysels
  • Christian Gouriéroux
  • Joanna Jasiak

The purpose of the paper is to propose an autocorrelogram estimation procedure for irregularly spaced data which are modelled as subordinated continuous time series processes. Such processes, also called time deformed stochastic processes, have been discussed in a variety of contexts. Before entertaining the possibility of modelling such time series one is interested in examining simple diagnostics and data summaries. With continuous time processes this is a challenging task which can be accomplished via kernel estimation. This paper develops the conceptual framework, the estimation procedure and its asymptotic properties. An illustrative empirical example is also provided. L'objectif de cet article est de proposer une procédure d'estimation des autocorrélations pour les processus échantillonnés à des intervalles inégaux, modélisés comme processus subordonnés en temps continu. Ces processus, que l'on appelle aussi processus avec déformation du temps, ont été proposés dans plusieurs contextes. Avant d'élaborer sur la possibilité de modélisation des séries temporelles de ce type, on s'intéresse tout d'abord au diagnostic et à l'analyse des statistiques descriptives. Dans le domaine des processus en temps continu, cette difficile tâche peut être accomplie en ayant recours à la méthode d'estimation de l'autocorrélation par noyau. Cet article présente le cadre conceptuel, la procédure d'estimation et ses propriétés asymptotiques. Pour illustrer, un exemple empirique est aussi inclus.

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Paper provided by CIRANO in its series CIRANO Working Papers with number 96s-19.

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Length: 41 pages
Date of creation: 01 Jul 1996
Handle: RePEc:cir:cirwor:96s-19
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  1. Ghysels, E. & Jasiak, J., 1994. "Stochastic Volatility and time Deformation: an Application of trading Volume and Leverage Effects," Cahiers de recherche 9403, Universite de Montreal, Departement de sciences economiques.
  2. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339 Elsevier.
  3. HÄRDLE, Wolfgang & VIEU, Philippe, "undated". "Kernel regression smoothing of time series," CORE Discussion Papers RP 981, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Eric Ghysels & Andrew Harvey & Éric Renault, 1995. "Stochastic Volatility," CIRANO Working Papers 95s-49, CIRANO.
  5. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
  6. Gouriéroux, Christian & Monfort, Alain & Tenreiro, Carlos, 1994. "Kernel m-estimators : non parametric diagnostics for structural models," CEPREMAP Working Papers (Couverture Orange) 9405, CEPREMAP.
  7. Robinson, P M, 1988. "Semiparametric Econometrics: A Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(1), pages 35-51, January.
  8. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
  9. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
  10. Florens, Jean-Pierre & Mouchart, Michel, 1985. "A Linear Theory for Noncausality," Econometrica, Econometric Society, vol. 53(1), pages 157-175, January.
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