Option Pricing Techniques: Using Neural Networks

With the emergence of more complex option pricing models, the demand for fast and accurate numerical pricing techniques is increasing. Due to a growing amount of accessible computational power, neural networks have become a feasible numerical method for approximating solutions to these pricing models. This work concentrates on analysing various...

Forecasting the implied volatility surface in risk-management applications

In risk-management, one typically simulates many states of the market using models that are in line with historical data, also known as real-world models. For example, new regulations require insurance companies to value their position on a 1-year horizon. Insurance companies issue guarantees that need to be valued according to market...

Numerical Solutions for the Stochastic Local Volatility Model

This thesis is about pricing European options and forward start options under the Heston LSV model. The impact of conditionally calibrating the Heston parameters on the satisfaction of the Feller condition and thereafter correcting with a local volatility surface is investigated here. The results show that this approach is computationally time...

The Gibbs phenomenon in option pricing methods: Filtering and other techniques applied to the COS method

There are situations in which the COS method for option pricing has relatively slow convergence as a consequence of the Gibbs phenomenon. This thesis focusses on various methods to improve the convergence rate of the so called spectral methods. Note that we are not just interested in an accurate recovery, but that we also want to be able to...