Volatility Analysis of Stock Returns for Fifteen Listed Banks in Chittagong Stock Exchange

Abstract

The aim of the study is to analyze and prediction of return for 15 popular banks in Chittagong Stock Exchange. The economic development of a country depends largely on the effective performance of stock market. In this study, secondary data from the CSE, Bangladesh with a sample period 1st January 2009 to 27th December 2015 for selected 15 banks, listed in Chittagong Stock Exchange. Descriptive statistics, important graphs, statistical tests, fitted dynamic regression models with ARCH effect are used to complete the analysis. It is found that for all banks, the return occurs high with a high risk and risk is low for the companies with small amount of return. The daily log returns for all companies are almost normally distributed. Checking the stationarity of the log returns data getting from all banks in both graphical and statistical unit root method, time series data are found to be stationary. In the dynamic regression model the log return Yt is considered as dependent variable and the log daily average Xt is considered as independent variable. The average VIF for the returns of all banks are found less than 10, indicate not severity of multicollinearity and ∆Yt , ∆2Yt , ∆Xt , ∆2Xt can be used as the explanatory variables in the model where ∆ indicates the difference operator. Lagrange multiplier (LM) test based on the residuals of the regression model is significant for all the banks implies that the data have the conditional heteroscadisticity in the behavior of their residuals. The line diagrams conferred the complete randomness in Parkinson’s monthly volatility for every company. The log return of six out of 15 banks have significant ARCH effect with 2 period lags and rest of the banks, the log returns have significant ARCH effect with 1 period lag. The regression coefficients of and have the negative effects on and the other coefficients have both positive and negative effect. A modified ARDL (2,2) model is proposed and 1-step ahead forecasted model for different banks are recommended.One can try to estimate the confidence interval for the parameters used in modified model in his/her advanced research. Moreover, the other dynamic models such as GARCH, TGARCH, PARCH, EGARCH model and different dynamic panel data models such as Areonalo bond could be try to predict the data. Moreover, the other multivariate analysis such as canonical correlation analysis, factor analysis, cluster analysis and discriminant analysis can be done for further research on these data.