A Time Varying Parameter Model to Test for Predictability and Integration in Stock Markets of Transition Economies

This paper introduces a model, based on the Kalman filter framework, which allows for time varying parameters, latent factors, and a general GARCH structure for the residuals. With this extension of the Bekaert and Harvey (1997) model it is possible to test if an emerging stock market becomes more efficient over time and more integrated with other already established markets in situations where no macroeconomic conditioning variables are available. We apply this model to the Czech, Polish, Hungarian, and Russian stock markets. We use data at daily frequency running from April 7th 1994 to July 10th 1997. A latent factor captures macroeconomic expectations. Concerning predictability, measured with time varying autocorrelations, Hungary reached efficiency before 1994. Russia shows signs of ongoing convergence towards efficiency. For Poland and the Czech Republic we find no improvements. With regard to market integration there is evidence that the importance of Germany has changed over time for all markets. Shocks in the UK are positively felt on the Czech and Polish markets but not on the Russian or the Hungarian ones. Shocks in the US have no impact on these markets but the Russian one. A strong negative correlation between Russia and the US and Germany tends to disappear. We also show that for the transition economies under investigation stock returns exhibit significant asymmetric GARCH effects where bad news generate greater volatility. The exception to this rule is Hungary where good news cause greater volatility than bad volatility. This leads us to formulate a liquidity hypothesis.