This project solves the European-type basket option pricing problem. Finding analytical solutions or stable numerical schemes for the corresponding high-dimensional PDE is still an open problem. Hanbali and Linders (2019) propose an approximation of the problem using the element of comonotonicity. Their theoretical results have been further strengthened by Ling (2019) using a modern machine learning approach with tremendous improvement in terms of computation time without deteriorating numerical solutions much. In view of these, this project aims to relax the full comonotonicity approximation in Hanbali and Linders (2019), to reduce the pricing error arising from dependence approximation, while to implement modern machine learning approaches, to rectify the expenses in computation time due to a more realistic approximated dependence structure.
Students: Ruizheng Bai, Kara Wong
Supervisor: Alfred Chong, Daniël Linders
Graduate Supervisor: Biwen Ling