[2603.26318] STN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing

Quantitative Finance > Pricing of Securities arXiv:2603.26318 (q-fin) [Submitted on 27 Mar 2026] Title:STN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing Authors:Dominic Gribben, Carolina Allende, Alba Villarino, Aser Cortines, Mazen Ali, Román Orús, Pascal Oswald, Noureddine Lehdili View a PDF of the paper titled STN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing, by Dominic Gribben and 7 other authors View PDF Abstract:We develop a tensor-network surrogate for option pricing, targeting large-scale portfolio revaluation problems arising in market risk management (e.g., VaR and Expected Shortfall computations). The method involves representing high-dimensional price surfaces in tensor-train (TT) form using TT-cross approximation, constructing the surrogate directly from black-box price evaluations without materializing the full training tensor. For inference, we use a Laplacian kernel and derive TT representations of the kernel matrix and its closed-form inverse in the noise-free setting, enabling TT-based Gaussian process regression without dense matrix factorization or iterative linear solves. We found that hyperparameter optimization consistently favors a large kernel length-scale and show that in this regime the GPR predictor reduces to multilinear interpolation for off-grid inputs; we also derive a low-rank TT representation for this limit. We evaluate the approach on five-asset basket options over an eight dimension...