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Nuisance parameters, composite likelihoods and a panel of GARCH models

  • Cavit Pakel

    ()

    (Department of Economics and Oxford-Man Institute, University of Oxford, Oxford)

  • Neil Shephard

    ()

    (Oxford-Man Institute and Department of Economics, University of Oxford, Oxford)

  • Kevin Sheppard

    ()

    (Oxford-Man Institute and Department of Economics, University of Oxford, Oxford)

We investigate the properties of the composite likelihood (CL) method for (T ×N_T ) GARCH panels. The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across N_T series, other parameters of interest are assumed to be common. CL pools information across the panel instead of using information available in a single series only. Simulations and empirical analysis illustrate that in reasonably large T CL performs well. However, due to the estimation error introduced through nuisance parameter estimation, CL is subject to the “incidental parameter” problem for small T.

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File URL: http://www.nuffield.ox.ac.uk/economics/papers/2009/w12/cavit300909.pdf
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Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2009-W12.

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Length: 22 pages
Date of creation: 02 Oct 2009
Date of revision:
Handle: RePEc:nuf:econwp:0912
Contact details of provider: Web page: http://www.nuff.ox.ac.uk/economics/

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  12. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
  13. Ole E. Barndorff-Nielsen & Neil Shephard, 2000. "Econometric analysis of realised volatility and its use in estimating stochastic volatility models," Economics Papers 2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
  14. BAUWENS, Luc & ROMBOUTS, Jeroen, 2003. "Bayesian clustering of many GARCH models," CORE Discussion Papers 2003087, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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