Post-COVID Volatility Modelling and Forecasting of Global Stock Market Indices Using GARCH and Machine Learning

The pandemic significantly disrupted Global Financial Markets, which had never experienced such unusual levels of volatility and uncertainty since the Covid-19 outbreak. The post-recovery phase saw continued price fluctuations due to inflation, geopolitical conflicts, and economic policy changes.This study examines the volatility and market behaviour of six major global stock market indices — S&P 500, FTSE 100, DAX, Nikkei 225, NIFTY 50, and Shanghai Composite — using daily data from January 2019 to April 2026. The data is divided into three sub-periods: pre-COVID (2019), COVID (2020), and post-COVID (2021–2026). A combination of statistical, econometric, clustering, and machine learning methods is applied, including descriptive statistics, correlation analysis, ADF test, ARCH LM test, GARCH (1,1), K-Means clustering, PCA, Random Forest, XGBoost, and Support Vector Regression (SVR) with an RBF kernel. The GARCH (1,1) model confirmed high volatility persistence across all markets. Among the machine learning models, Random Forest achieved the best overall prediction performance with a mean R² of 0.7678, mean RMSE of 0.003384, and mean MAE of 0.001973, followed by XGBoost (R²=0.7565) and SVR (R²=0.5851). The findings indicate that the COVID-19 pandemic was associated with persistent changes in global stock market volatility and behaviour that extended into the post-COVID period.