Title:	Machine Learning Approaches for High-Dimensional Option Pricing and Hedging
DNr:	NAISS 2025/22-690
Project Type:	NAISS Small Compute
Principal Investigator:	Ying Ni <ying.ni@mdu.se>
Affiliation:	Mälardalens universitet
Duration:	2025-05-07 – 2026-06-01
Classification:	10105
Keywords:

Abstract

This project is in the field of financial mathematics and in particular computational finance. The aim is to advance the fields of option pricing and hedging by applying state-of-the-art machine learning techniques, with a focus on computationally intensive financial derivatives. The first component addresses the pricing of Bermudan basket options in high-dimensional settings. The second component of the project centers on option hedging. Building on the deep hedging paradigm, we test a novel hedging framework that integrates machine learning tools with user-centered explainability and high hedging performance under advanced stochastic market models. Both components of the project involve extensive simulations, high-dimensional modeling, and training of large-scale machine learning models, necessitating access to high-performance computing resources. The proposed research has the potential to significantly advance the computational methods used in quantitative finance, particularly in scenarios where dimensionality and model complexity pose major challenges.