Evaluating the Efficacy of GARCH Models in Forecasting Volatility Dynamics Across Major Global Financial Indices: A Decade-long Analysis

Abstract

This study investigates the volatility dynamics of five major global financial indices—FTSE 100, Hang Seng, NIKKEI 225, NSE 50, and S&P 500—using a range of GARCH models over a ten-year period from January 1, 2014, to December 31, 2023. The analysis involves preprocessing the data to ensure stationarity, calculating log returns, and conducting stationarity and ARCH effect LM tests. Various GARCH models, including GARCH (0,1), GARCH (1,1), GARCH (1,2), and GARCH (2,2), are applied to capture and forecast volatility. The study aims to determine the most effective model for accurately reflecting volatility dynamics while accounting for significant market events such as the COVID-19 pandemic. The findings reveal that the GARCH (1,1) model generally provides a robust balance between model simplicity and statistical significance, effectively capturing the time-varying volatility of the indices. Despite some complex models offering better fit measures according to the Akaike Information Criterion (AIC) and Schwarz Criterion (SC), the GARCH (1,1) model consistently demonstrates significant parameter estimates and reliable predictive performance, as evidenced by consistent Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This suggests that the GARCH (1,1) model is a preferred choice for volatility forecasting due to its effectiveness and parsimony, although future research might explore more advanced GARCH model variations for further refinement.