An unsupervised deep learning approach to solving partial integro-differential equations

DOI10.1080/14697688.2022.2057870zbMath1497.91306OpenAlexW3037862043WikidataQ114098665 ScholiaQ114098665MaRDI QIDQ5092661

Weilong Fu, Ali Hirsa

Publication date: 22 July 2022

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2022.2057870

zbMATH Keywords

neural networkoption pricingLévy processdeep learningPIDE

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Artificial neural networks and deep learning (68T07) Integro-partial differential equations (45K05) Derivative securities (option pricing, hedging, etc.) (91G20)

Cites Work

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• Processes of normal inverse Gaussian type
• DGM: a deep learning algorithm for solving partial differential equations
• A neural network-based framework for financial model calibration
• Solving the Kolmogorov PDE by means of deep learning
• Neural algorithm for solving differential equations
• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
• Solving high-dimensional partial differential equations using deep learning
• Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models
• A Fast and Accurate FFT-Based Method for Pricing Early-Exercise Options under Lévy Processes
• Learning representations by back-propagating errors
• Option pricing when underlying stock returns are discontinuous
• A Finite Difference Scheme for Option Pricing in Jump Diffusion and Exponential Lévy Models
• On the distribution of points in a cube and the approximate evaluation of integrals