The dynamics of financial market volatility have long captured the interest of researchers and practitioners alike, particularly due to its implications for risk management, portfolio allocation, and derivatives pricing. In this working paper read about the predictive performance of asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models—specifically the VS-GARCH, GJR-GARCH, and Q-GARCH variants—in comparison to the symmetric GARCH(1,1) benchmark. Focusing on three Asian and ten European stock market indices, this study evaluates volatility forecasts using classical performance metrics and innovative forecast combination techniques. By addressing the asymmetric effects of market shocks on conditional variance, the paper provides valuable insights into the suitability of these models for diverse market environments. While the in-sample combination methods show promise, the results reveal challenges in extending these findings to out-of-sample contexts. This paper not only contributes to the broader discourse on volatility forecasting but also sets the stage for future research into multivariate and adaptive modelling frameworks that can better capture the complexities of financial markets.