Forecasting the Stock Market Returns Using nonlinear hybrid GARCH-SETAR model

Abstract

Forecasting stock market returns is a valuable tool for investors seeking to enhance their gains in stock trading. Predicting stock prices proves to be a formidable endeavor due to its substantial volatility, non-linear characteristics trends, and responsiveness to multifaceted variables, including economic conditions, market trends, seasonality, and sentiment. Despite these complexities, non-linear methodologies like threshold time series and conditional heteroscedasticity models are underutilized. This study aims to assess the predictive capabilities of a hybrid GARCH-SETAR model in the context of stock market returns, encompassing both Islamic and conventional stocks listed on the Pakistan Stock Exchange. The Islamic and conventional stock markets in Pakistan represented by KMI-30, KSE-30 and KSE-100 selected contain daily data from January, 2012 till June, 2023. After the confirmation of stationarity, ARCH effect and non-linearity by ADF, PP unit root, ARCH-LM and BDS test respectively the best estimated linear traditional Box-Jenkins ARIMA, non-linear threshold SETAR and ARIMA-GARCH models are selected based on AIC and BIC information criteria’s. The best proposed hybrid GARCH-SETAR model is also selected based on AIC and BIC information criteria’s. It is revealed that the one-step-ahead recursive forecast performance proposed hybrid GARCH-SETAR model outperforms all other selected linear and non-linear models for both Islamic and conventional stock markets based on RMSE, MAE, MAPE and SMAPE forecast evaluation criteria’s.

Keywords

• Stock returns, KSE-30, KMI-30, KSE-100, forecast performance, proposed hybrid model