A hybrid ExpAR-FIGARCH-ANN model for time series forecasting

Financial time series forecast is challenging due to nonlinear mean dynamics, volatility clustering, and long-memory effects. Traditional hybrid models such as Autoregressive Integrated Moving Average – Generalised Autoregressive Conditional Heteroscedasticity (ARIMA–GARCH) and Fractional Generalised Integrated Autoregressive Conditional Heteroscedasticity – Artificial Neural Network (FIGARCH–ANN), improve forecasting performance but remain limited by linear mean assumptions, short-memory volatility, or incomplete treatment of nonlinearities. These constraints are particularly evident in emerging markets like Nigeria, where financial returns display pronounced nonlinear and persistent volatility patterns. Thus, this study developed a hybrid model to address volatility, nonlinearity, and long memory in residuals. Daily Nigeria All Share Stock Index Data (2001-2019), exhibiting these characteristics was used to assess the forecast performance of the new hybrid Exponential Autoregressive – Fractional Integrated Generalized Autoregressive Conditional Heteroscedasticity – Artificial Neural Network (ExpAR-FIGARCH-ANN) model in comparison to the existing Exponential Autoregressive – Fractional Integrated Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-FIGARCH) and Artificial Neural Network (ANN) models using error-based metrics, viz Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Mean Squared Error (MSE). The empirical findings show that the hybrid ExpAR-FIGARCH-ANN model outperformed the standalone ExpAR-FIGARCH and ANN model. It achieved the lowest error metrics (MSE = 0.0029, MAE = 0.0352, MAPE = 1.68%), confirming superior predictive performance. This enhanced performance is ascribed to the novel capability of the model to concurrently address nonlinear mean dynamics, long-memory volatility, and residual nonlinearities. It provides a more accurate forecast than existing hybrid models, thus, has potential applications beyond stock indices.