IDEAS home Printed from https://ideas.repec.org/p/cir/cirwor/96s-19.html
   My bibliography  Save this paper

Kernel Autocorrelogram for Time Deformed Processes

Author

Listed:
  • Eric Ghysels
  • Christian Gouriéroux
  • Joanna Jasiak

Abstract

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.

Suggested Citation

  • Eric Ghysels & Christian Gouriéroux & Joanna Jasiak, 1996. "Kernel Autocorrelogram for Time Deformed Processes," CIRANO Working Papers 96s-19, CIRANO.
  • Handle: RePEc:cir:cirwor:96s-19
    as

    Download full text from publisher

    File URL: http://www.cirano.qc.ca/files/publications/96s-19.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    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. Hardle, W. & Vieu, P., 1990. "Kernel regression smoothing of time series," CORE Discussion Papers 1990031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    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. Robinson, P M, 1988. "Semiparametric Econometrics: A Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(1), pages 35-51, January.
    7. 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.
    8. 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.
    9. Florens, Jean-Pierre & Mouchart, Michel, 1985. "A Linear Theory for Noncausality," Econometrica, Econometric Society, vol. 53(1), pages 157-175, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christian Gourieroux & Gaëlle Le Fol, 1997. "Volatilités et mesures de risque," Post-Print halshs-00877048, HAL.

    More about this item

    Keywords

    Subordinated Processes; Irregularly Spaced Data; Continuous Time Processes; Nonparametric Methods; Processus subordonnés; Observations manquantes; Processus en temps continu; Méthodes non paramétriques;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cir:cirwor:96s-19. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Webmaster). General contact details of provider: http://edirc.repec.org/data/ciranca.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.