Forecasting Index Return Volatility of The Chittagong Stock Exchange of Bangladesh using GARCH Models

Abstract

Purpose: The aim of this research is to identify the best-fitted model(s) for estimating and forecasting the return volatility of the Chittagong Stock Exchange (CSE) in Bangladesh. Methodology: The study analyzes the returns of the Chittagong Stock Exchange's (CSE) daily Selective Categories Index (CSCX) from February 4, 2013 to December 31, 2021 (as a full sample) and from July 1, 2021 to December 30, 2021 (for forecasting). The researcher used GARCH family approaches considering different error distributions, to find the well-suited model(s) for the CSCX index. The researchers used ARMA to develop the mean equation based on two popular model selection criteria: Schwarz's (1978) Bayesian information criterion (SBIC) and Akaike's (1974) information criterion (AIC). The data has been analyzed using the application software E-Views 10. Findings: The ARMA (0,1) has been adopted as the mean equation for GARCH specifications. Under all three types of error distributions, the ARCH and GARCH terms, along with the leverage terms of asymmetric models, were found to be statistically significant in all the accepted combinations of the model. The models GARCH (1,2), TGARCH (1,2), and PARCH (1,2) under generalized error distributions and EGARCH (2,1) under Student’s t error distributions have been selected as the best-fitted models for estimation. Whereas, based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), and theil inequality (TI), EGARCH (1, 2), TGARCH (1, 2), and PARCH (1, 2) under generalized error distributions, and GARCH (1, 2) under student’s t error distributions and normal error distributions are found to have superior out-of-sample forecasting abilities. Practical implications and originality: This is an original research work that will help the investors and other stakeholders of the Bangladeshi stock market to estimate and forecast market volatility more efficiently. Limitations: Due to its extensive features, this study was unable to incorporate a few additional ARCH and GARCH models.