Volatility estimation is an important aspect when analysing market movements. The purpose of this paper is to study the ability of symmetric and asymmetric GARCH models to investigate the volatility of the FTSE 100 Implied Volatility Index (IVI). We use several alternative models including GARCH, EGARCH, GJR-GARCH, and the mixed data sampling approach, GARCH-MIDAS to model the conditional variance. We also introduce FTSE 100 index returns and several macroeconomic variables into the analysis to investigate whether they have an explanatory role in defining the conditional variance. The macroeconomic variables are the UK industrial production, 3 months LIBOR, GBP effective exchange rate, and unemployment rate. The results show that market returns and macroeconomic factors exhibit an important role in modelling the volatility for IVI indices, especially when using GARCH (1,1), which outperformed other models. Our results show that market returns and macroeconomic factors exhibit an important role in modelling the volatility for IVI indices, especially when using GARCH (1,1), which outperformed other models. The GARCH-MIDAS approach also presented evidences of the effect of these explanatory factors on estimating IVI.