Garch Model Hybridization With Feed Forward Neural Network Algorithm Approach For Predicting The Volatility Of The Composite Stock Price Index

Abstract

Stock market volatility is a crucial indicator in measuring investment risk and influencing investor decision-making, where proper understanding of volatility movements can help investors optimize their investment portfolios. Time series data from the stock exchange shows complex heteroscedasticity characteristics, where volatility levels can change dynamically over time, creating distinct challenges in modeling and prediction. The implementation of the hybrid model is carried out by integrating the advantages of both models, where GARCH is used to capture volatility clustering characteristics, while FFNN is utilized to capture complex non-linear patterns in the data.

By using evaluation of several comprehensive error measurement metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), to ensure model reliability in various aspects of prediction. The use of the GARCH-FFNN hybrid model is expected to provide more accurate volatility predictions compared to using GARCH or FFNN models separately, with potential improvements in prediction accuracy and adaptability to changing market conditions. These findings provide important contributions to stock market volatility modeling and can serve as a reference for investors, portfolio managers, and financial practitioners in making better investment decisions, as well as paving the way for the development of more sophisticated volatility prediction models in the future