GARCH models and an application to stock return volatility with the effect of daily trading volume in Istanbul Securities Exchange

Abstract

In this study, the effect of daily trading volume on stock return volatility is analyzed using the data from Istanbul Securities Exchange (ISE). Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process is employed to model the persistence in volatility of daily returns and to capture the relation between daily price increments and the trading volume. Results approve the consistency of GARCH process in modeling stock returns and indicate positive relation between the volatility of daily returns and trading volume. Also, a reduction of persistence in volatility is observed with the inclusion of trading volume in the model.