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A Comparison Of Mean-Variance Efficiency Tests

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  • Enrique Sentana

    ()
    (CEMFI, Centro de Estudios Monetarios y Financieros)

  • Dante Amegual

    ()
    (Princeton University)

Abstract

We analyse the asymptotic properties of mean-variance efficiency tests based on generalised methods of moments, and parametric and semiparametric likelihood procedures that assume elliptical innovations. We study the trade-off between efficiency and robustness, and prove that the fully parametric estimators provide asymptotically valid inferences when the conditional distribution of the innovations is elliptical but possibly misspecificed and heteroskedastic. We compare the small sample performance of the alternative tests in a Monte Carlo study, and find some discrepancies with their asymptotic properties. Finally, we present an empirical application to US stock returns, which rejects the mean-variance efficiency of the market portfolio.

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Bibliographic Info

Paper provided by CEMFI in its series Working Papers with number wp2008_0806.

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Date of creation: Apr 2008
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Handle: RePEc:cmf:wpaper:wp2008_0806

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Keywords: Adaptivity; elliptical distributions; financial returns; portfolio choice; semiparametric estimators.;

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  1. Douglas J Hodgson & Oliver Linton & Keith Vorkink, 2000. "Testing the Capital Asset Pricing Model Efficiently under Elliptical Symmetry: A Semiparametric Approach," STICERD - Econometrics Paper Series /2000/398, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  2. Fiorentini, Gabriele & Sentana, Enrique & Calzolari, Giorgio, 2003. "Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 532-46, October.
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  5. MEDDAHI, Nour & RENAULT, Éric, 1998. "Quadratic M-Estimators for ARCH-Type Processes," Cahiers de recherche 9814, Universite de Montreal, Departement de sciences economiques.
  6. Roon, F.A. de & Nijman, T.E., 1998. "Testing for mean-variance spanning: A survey," Discussion Paper 1998-132, Tilburg University, Center for Economic Research.
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  8. Huberman, Gur & Kandel, Shmuel, 1987. " Mean-Variance Spanning," Journal of Finance, American Finance Association, vol. 42(4), pages 873-88, September.
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  10. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc.
  11. Gabriele Fiorentini & Enrique Sentana, 2007. "On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models," Working Paper Series 38-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
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  14. Javier Mencía & Enrique Sentana, 2012. "Distributional Tests in Multivariate Dynamic Models with Normal and Student-t Innovations," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 133-152, February.
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  16. Douglas J. Hodgson & Oliver Linton & Keith Vorkink, 2004. "Testing forward exchange rate unbiasedness efficiently: a semiparametric approach," Journal of Applied Economics, Universidad del CEMA, vol. 0, pages 325-353, November.
  17. Douglas Hodgson, 2000. "Unconditional pseudo-maximum likelihood and adaptive estimation in the presence of conditional heterogeneity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 19(2), pages 175-206.
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