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Kernel Based Nonlinear Canonical Analysis

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Author Info

  • Serge Darolles

    (Crest)

  • Jean-Pierre Florens

    (Crest)

  • Christian Gourieroux

    (Crest)

Abstract

We consider a kernel based approach to nonlinear canonical correlation analysis and its implementation for time series. We deduce various diagnostics for reversible processes and gaussian processes. The method is first applied to a stimulated series satisfying a diffusion equation allowing us to estimate nonparametrically the drift and volatility functions. The second application involves high frequency data on stock returns.

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Bibliographic Info

Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 98-55.

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Date of creation: 1998
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Handle: RePEc:crs:wpaper:98-55

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Cited by:
  1. Xiaohong Chen & Lars Peter Hansen & Jos´e A. Scheinkman, 2005. "Principal Components and the Long Run," Levine's Bibliography 122247000000000997, UCLA Department of Economics.
  2. Darolles, Serge & Gourieroux, Christian, 2001. "Truncated dynamics and estimation of diffusion equations," Journal of Econometrics, Elsevier, vol. 102(1), pages 1-22, May.
  3. Nour Meddahi, 2001. "An Eigenfunction Approach for Volatility Modeling," CIRANO Working Papers 2001s-70, CIRANO.
  4. Xiaohong Chen & Lars Peter Hansen & Jose Scheinkman, 2009. "Principal Components and Long Run Implications of Multivariate Diffusions," Cowles Foundation Discussion Papers 1694, Cowles Foundation for Research in Economics, Yale University.
  5. Serge Darolles & Jean-Pierre Florens & Eric Renault, 2000. "Nonparametric Instrumental Regression," Working Papers 2000-17, Centre de Recherche en Economie et Statistique.
  6. Beare, Brendan K. & Seo, Juwon, 2012. "Time irreversible copula-based Markov Models," University of California at San Diego, Economics Working Paper Series qt31f8500p, Department of Economics, UC San Diego.
  7. Nour Meddahi, 2000. "Temporal Aggregation of Volatility Models," Econometric Society World Congress 2000 Contributed Papers 1903, Econometric Society.

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