Forecasting stock market volatility using GARCH models: A comparative study of the U.S. and Saudi markets

Abstract

The paper analyzes the volatility trends of the Saudi Arabian Tadawul All Share Index (TASI) and the S&P 500 index, focusing on the COVID-19 pandemic as a key market shock. The analysis incorporates daily stock return data covering the period from January 2015 to May 2025. The volatility of emerging and developed markets is examined through EGARCH and GARCH approaches to study characteristics such as volatility clustering and asymmetry. The effect of the pandemic is directly embedded by introducing COVID-19 dummy variables into the models. Empirical findings suggest that both indices are characterized by volatility clustering, and the EGARCH model is more appropriate than the GARCH model for estimating asymmetric volatility, particularly during crisis periods. Additionally, the COVID-19 dummy variable is statistically significant in the EGARCH model, as opposed to the GARCH model. The results support the leverage effect, indicating that negative shocks have a more significant impact on market volatility than positive ones. The S&P 500 showed a faster recovery after the COVID-19 crisis, whereas TASI was slower in mean reversion, indicating structural and behavioral divergence between the markets. This comparative study contributes to the literature by providing a clear picture of volatility dynamics in diverse financial contexts and highlighting the superiority of EGARCH models during crisis periods. The findings offer guidance to policymakers aiming to improve market stability and to investors seeking diversification into both developing and mature markets.