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Semiparametric multivariate volatility models

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  • Rombouts, Jeroen V. K.
  • Hafner, Christian M.

Abstract

Estimation of multivariate volatility models is usually carried out by quasi maximum likelihood (QMLE), for which consistency and asymptotic normality have been proven under quite general conditions. However, there may be a substantial efficiency loss of QMLE if the true innovation distribution is not multinormal. We suggest a nonparametric estimation of the multivariate innovation distribution, based on consistent parameter estimates obtained by QMLE. We show that under standard regularity conditions the semiparametric efficiency bound can be attained. Without reparametrizing the conditional covariance matrix (which depends on the particular model used), adaptive estimation is not possible. However, in some cases the e?ciency loss of semiparametric estimation with respect to full information maximum likelihood decreases as the dimension increases. In practice, one would like to restrict the class of possible density functions to avoid the curse of dimensionality. One way of doing so is to impose the constraint that the density belongs to the class of spherical distributions, for which we also derive the semiparametric efficiency bound and an estimator that attains this bound. A simulation experiment demonstrates the e?ciency gain of the proposed estimator compared with QMLE. --

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Bibliographic Info

Paper provided by Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) in its series Papers with number 2004,14.

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Date of creation: 2004
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Handle: RePEc:zbw:caseps:200414

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Keywords: Multivariate volatility; GARCH; semiparametric efficiency; adaptivity;

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References

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  1. Douglas J. Hodgson & Oliver Linton & Keith Vorkink, 2001. "Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach," Cahiers de recherche CREFE / CREFE Working Papers 143, CREFE, Université du Québec à Montréal.
  2. González-Rivera, G. & Drost, F.C., 1998. "Efficiency Comparisons of Maximum Likelihood-Based Estimators in GARCH Models," Discussion Paper 1998-124, Tilburg University, Center for Economic Research.
  3. Song Chen, 2000. "Probability Density Function Estimation Using Gamma Kernels," Annals of the Institute of Statistical Mathematics, Springer, vol. 52(3), pages 471-480, September.
  4. Comte, F. & Lieberman, O., 2003. "Asymptotic theory for multivariate GARCH processes," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 61-84, January.
  5. Douglas J. Hodgson & Keith Vorkink, 2001. "Efficient Estimation of Conditional Asset Pricing Models," Cahiers de recherche CREFE / CREFE Working Papers 144, CREFE, Université du Québec à Montréal.
  6. Berk, Jonathan B., 1997. "Necessary Conditions for the CAPM," Journal of Economic Theory, Elsevier, vol. 73(1), pages 245-257, March.
  7. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  8. Drost, F.C. & Klaassen, C.A.J. & Werker, B.J.M., 1994. "Adaptive estimation in time-series models," Discussion Paper 1994-88, Tilburg University, Center for Economic Research.
  9. HAFNER, Christian M. & HERWARTZ, Helmut, 1998. "Volatility impulse response functions for multivariate GARCH models," CORE Discussion Papers 1998047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. Gloria Gonzalez-Rivera, 1997. "A note on adaptation in garch models," Econometric Reviews, Taylor & Francis Journals, vol. 16(1), pages 55-68.
  11. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-59, October.
  12. Jeantheau, Thierry, 1998. "Strong Consistency Of Estimators For Multivariate Arch Models," Econometric Theory, Cambridge University Press, vol. 14(01), pages 70-86, February.
  13. Drost, F.C. & Klaassen, C.A.J., 1997. "Efficient estimation in semiparametric GARCH models," Open Access publications from Tilburg University urn:nbn:nl:ui:12-74146, Tilburg University.
  14. Linton, Oliver, 1993. "Adaptive Estimation in ARCH Models," Econometric Theory, Cambridge University Press, vol. 9(04), pages 539-569, August.
  15. Newey, Whitney K, 1990. "Semiparametric Efficiency Bounds," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(2), pages 99-135, April-Jun.
  16. Steigerwald, Douglas G., 1992. "Adaptive estimation in time series regression models," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 251-275.
  17. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
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Citations

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Cited by:
  1. Gabriele Fiorentini & Enrique Sentana, 2012. "Tests For Serial Dependence In Static, Non-Gaussian Factor Models," Working Papers wp2012_1211, CEMFI.
  2. Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Multivariate GARCH models," CREATES Research Papers 2008-06, School of Economics and Management, University of Aarhus.
  3. Enrique Sentana & Gabriele Fiorentini, 2007. "On The Efficiency And Consistency Of Likelihood Estimation In Multivariate Conditionally Heteroskedastic Dynamic Regression Models," Working Papers wp2007_0713, CEMFI.
  4. Hafner, Christian M. & Preminger, Arie, 2009. "Asymptotic Theory For A Factor Garch Model," Econometric Theory, Cambridge University Press, vol. 25(02), pages 336-363, April.
  5. Mark J. Jensen & John M. Maheu, 2012. "Bayesian semiparametric multivariate GARCH modeling," Working Paper 2012-09, Federal Reserve Bank of Atlanta.
  6. Rombouts, J.V.K. & Verbeek, M.J.C.M., 2009. "Evaluating Portfolio Value-At-Risk Using Semi-Parametric GARCH Models," ERIM Report Series Research in Management ERS-2004-107-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  7. BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen, 2003. "Multivariate GARCH models: a survey," CORE Discussion Papers 2003031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. Gabriele Fiorentini & Enrique Sentana, 2009. "Dynamic Specification Tests For Static Factor Models," Working Papers wp2009_0912, CEMFI.
  9. Hafner, Christian M. & Preminger, Arie, 2009. "On asymptotic theory for multivariate GARCH models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2044-2054, October.

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