We seek to estimate a portfolio of option prices in an entirely data driven way, at any future time, for trading and risk management purposes in a model independent way. We do not know the model driving the dynamics of the actual stock prices, but only observe discretely their evolution in the...

This textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural...

We propose to model the dynamics of the entire implied volatility surface (IVS) multi-step ahead by letting the parameters of a stochastic volatility model with an explicit expression for the smile be dynamically evolved. We assume that these model parameters are stochastic processes driven by...

When pricing options, correctly modelling the dynamics of the underlying stock process, by accounting for all corporate events, is a challenge. The difficulties arise due to the resulting price gaps on the underlying process. Moreover, the resulting implied volatility surface is no-longer...