Forecasting the implied volatility surface using dynamic factor models with GARCH disturbances

The implied volatility surface (IVS) explains the dynamics between different option contracts by representing the total set of implied volatilities across moneyness and maturity dimensions. In this thesis, we implement dynamic factor models to study the dynamics of the IVS. In particular, we examine whether we can improve the fit of the IVS estimated by dynamic factor models by integrating additional volatility disturbances onto their residuals. In general, we provide four key findings. First, including GARCH disturbances appears to at least mitigate the problem of poorly fitting corner IVS groups by correcting for heteroskedasticity and autocorrelation in the error terms. Second, although our extended setups have a better in-sample fit, they are outperformed by the general dynamic factor model in terms of statistical and economical forecasting performances. Third, all our dynamic factor models for the IVS only have economic value when excluding transaction costs. Fourth, we hardly report significant differences between our dynamic factor models including GARCH disturbances.