Please note: This PhD defence will take place in DC 3317 and online.
Ke Nian, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Yuying Li
Options hedging is a critical problem in financial risk management. The prevailing approach in financial derivative pricing and hedging has been to firstly assume a parametric model describing the underlying price dynamics. An option model function is then calibrated to current market option prices and various sensitivities are computed and used to hedge the option risk. It has been recognized that computing hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing the variance of the option hedging risk, as it fails to capture the model parameter dependence on the underlying price.
We propose several data-driven approaches to directly learn a hedging function from the historical market option and underlying data by minimizing certain measure of the local hedging risk and total hedging risk. This thesis will focus on answering the following questions: 1) Can we efficiently build direct data-driven models for discrete hedging problem that outperform existing state-of-art parametric hedging models based on the market prices? 2) Can we incorporate feature selection and feature extraction into the data-driven models to further improve the performance of the discrete hedging? 3) Can we build efficient models for both the one-step local risk hedging problem and multi-steps total risk hedging problem based on the state-of-art learning framework such as deep learning framework and kernel learning framework?