Understanding volatility in emerging capital markets is important for determining the cost of capital and for evaluating direct investment and asset allocation decisions. We provide an approach that allows the relative importance of world and local information to change through time in both the expected returns and conditional variance processes. Our time-series and cross-sectional models analyze the reasons that volatility is different across emerging markets, particularly with respect to the timing of capital market reforms. We find that capital market liberalizations often increase the correlation between local market returns and the world market but do not drive up local market volatility.

We document extreme bias and dispersion in the small-sample distributions of four standard regression-based tests of the expectations hypothesis of the term structure of interest rates. The biases arise because of the extreme persistence in short interest rates. We derive approximate analytic expressions for the biases under a simple first-order autoregressive data generating process for the short rate. We then conduct Monte Carlo experiments based on a bias-adjusted first-order autoregressive process for the short rate and for a more realistic bias-adjusted VAR-GARCH model incorporating the short rate and three term spreads. Conducting inference with the small-sample distributions of test statistics rather than with their asymptotic distributions provides a more consistent rejection of the expectations hypothesis. Plausible sources of measurement error in short and long yields do not salvage the expectations hypothesis.