STN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing
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 ...
| Authors | Dominic Gribben, Carolina Allende, Alba Villarino et al. |
|---|---|
| Primary category | q-fin.PR |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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