Abstract
The purpose of this paper is to investigate the time-series and distributional properties of Central European stock returns. We test the random walk hypothesis and then consider an alternative to random walk - the ARIMA model for stock prices.
The behavior of volatility of returns over time is studied using the GARCH-t model which also allows us to learn more about the distribution properties of stock returns. We employ the BDS test to assess the ability of the estimated GARCH-t model to capture all nonlinearities in stock returns.
Our empirical findings reveal that the Czech and Hungarian stock market indices are predictable from the time series of historical prices, whereas that of Poland is not. The returns on all three indices are conditionally heteroskedastic and non-normal.
The estimated number of degrees of freedom ranges from 18 to 4.