Modelling Volatility of Daily Stock Returns: Is GARCH(1,1) Enough?

Abstract

Volatility in financial markets has attracted growing attention by academics, policy makers and practitioners during the past two decades. First, volatility receives a great deal of concern from policy makers and financial market participants because it can be used as a measurement of risk. Second, High volatility of return in financial market may discourage investors to invest in stock market and hence greater uncertainty. So we need to estimate the appropriate volatility model to capture the volatility. In this paper, we study the performance of simple GARCH model. We apply the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model of different lag order to model volatility of stock returns of four Bangladeshi Companies on Dhaka Stock Exchange (DSE). Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are used to select the best GARCH(p,q) model. From the empirical results, it is found that the distribution of daily returns are non-normal with negative skewness and pronounced excess kurtosis. Result shows that, GARCH(1,1) is  the best than other GARCH(p,q) models in modeling volatility for the daily return series of DSE.