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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Paper provided by CEMFI in its series Working Papers with number
wp2008_0806.
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
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