Black-Scholes and Augmented Option Pricing Models

The Black-Scholes model was a revelation and took a large step forward in terms of mathematical application in quantitative finance. An empirical trait is that the model has generally been used by practitioners in an ad-hoc fashion. This may explain why actual option prices have rarely converged to respective Black-Scholes estimates. Empirical options research has highlighted systematic biases within the model and has attempted to correct for these by proposing models that offer greater consistency in both internal processes and pricing performance. In this thesis, we explore the fundamental reasons for failure in the Black-Scholes and analyse the benefit of augmenting the model for processes that may be more consistent with the real world. We place emphasis on consistency between the option-implicit distribution of the underlying asset and the actual implicit distribution of the underlying asset. Using a three year FTSE 100 option dataset, we quantitatively examine the pricing consistency and reliability of such augmented models.