Low-Rank Tensor Approximation for Parametric Option Pricing

Dr Kathrin Glau, Queen Mary University London

Abstract 

Computationally intensive problems in finance are characterized by their intrinsic high-dimensionality which often is paired with optimizations leading to nonlinearities. While classical numerical methods typically suffer from a curse in dimensionality, machine learning approaches promise to yield fairly accurate results with a method that is scalable in the dimensions. Computational intense training phases and the required large set of training data pose some of the major challenges for the development of new and adequate numerical methods for finance. Merging classical numerical techniques with learning methods we propose a new approach to option pricing in parametric models. The work is based on [1] and ongoing research with Paolo Colusso and Francesco Statti.

‚Äč[1] Glau, K.; Kressner, D.; Statti, F.: Low-rank tensor approximation for Chebyshev interpolation in parametric option pricing.  preprint 2019