Random neural networks for rough volatility

Antoine Jacquier and Zan Zuric

Papers from arXiv.org

Abstract: We construct a deep learning-based numerical algorithm to solve path-dependent partial differential equations arising in the context of rough volatility. Our approach is based on interpreting the PDE as a solution to an SPDE, building upon recent insights by Bayer, Qiu and Yao, and on constructing a neural network of reservoir type as originally developed by Gonon, Grigoryeva, Ortega. The reservoir approach allows us to formulate the optimisation problem as a simple least-square regression for which we prove theoretical convergence properties.

Date: 2023-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-des and nep-rmg
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