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The efficient markets hypothesis in finance suggests that as equity markets are liberalized and made more open to the public, equity prices should reflect the increased availability of information and be more efficiently priced. In this paper, we examine whether emerging market equity prices have become more efficient after financial liberalization. Using two sets of financial liberalization dates, a battery of econometric tests, and data from sixteen countries and three composite portfolios, we find that in spite of theory suggesting the opposite, liberalization does not seem to have improved the efficiency of emerging markets. In fact, most of our statistical tests indicate that the markets were already efficient before the actual liberalization.

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We propose an EM algorithm to estimate ordered probit models with endogenous regressors. The proposed algorithm has a number of computational advantages in comparison to direct numerical maximization of the (limited information) log-likelihood function. First, the sequence of conditional M(aximization)-steps can all be computed analytically. Second, the algorithm updates the model parameters so that positive definiteness of the covariance matrix and monotonicity of cutpoints are naturally satisfied. Third, the variance parameters normalized for identification can be activated to accelerate convergence of the algorithm. The algorithm can be applied to models with dummy endogenous, continuous endogenous or latent endogenous regressors. A small Monte Carlo simulation experiment examines the finite sample performance of the proposed algorithms. Copyright The Author(s). Journal compilation Royal Economic Society 2009

Summary  I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state–space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi‐maximum likelihood and importance sampler filters. The result that the prediction step of the Gaussian filter can be evaluated analytically is shown to apply more generally to a number of commonly used specifications of the stochastic volatility model.