The study examined the distribution of equity returns by dividing the sample period into two
subperiods; periods before and after the market was opened to international investors. Return distributions
are studied by comparing the descriptive statistics of the Dhaka Stock Exchange Index (DSEI). Market
efficiency is examined with reference to the structure of autocorrelation (ARCH) in returns. In order to
examine the stochastic process over the study period, we employed models of conditional variances ...view middle of the document...
Moreover, conclusions regarding predictability of returns based on the significance of
autocorrelation coefficients are valid only after controlling for the ARCH effects (Errunza et al., 1994).
The Autoregressive Conditional Heteroskedasticity (ARCH) model introduced by Engle (1982)
allows the variance of the error term to vary over time, in contrast to the standard time series regression
models which assume a constant variance. Bollerslev (1986) generalized the ARCH process by allowing
for a lag structure for the variance. The generalized ARCH models, i.e. the GARCH models, have been
found to be valuable in modeling the time series behavior of stock returns (Baillie and DeGennaro, 1990;
Akgiray, 1989; French et al. 1987; Koutmos, 1992; Koutmos et al. 1993). Bollerslev (1986) allows the
conditional variance to be a function of prior period’s squared errors as well as of its past conditional
The GARCH model has the advantage of incorporating heteroscedasticity into the estimation
procedure. All GARCH models are martingale difference implying that all expectations are unbiased. The
GARCH models are capable of capturing the tendency for volatility clustering in financial data.