A neural network-based framework for financial model calibration

DOI10.1186/s13362-019-0066-7zbMath1461.91318arXiv1904.10523OpenAlexW3098509316WikidataQ127288021 ScholiaQ127288021MaRDI QIDQ2022121

Anastasia Borovykh, Lech A. Grzelak, Shuaiqiang Liu, Cornelis W. Oosterlee

Publication date: 27 April 2021

Published in: Journal of Mathematics in Industry (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.10523

zbMATH Keywords

global optimizationparallel computingartificial neural networksmodel calibrationmachine learningcomputational financeasset pricing model

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Artificial neural networks and deep learning (68T07) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (12)

The deep parametric PDE method and applications to option pricing ⋮ Deep calibration of financial models: turning theory into practice ⋮ An unsupervised deep learning approach to solving partial integro-differential equations ⋮ Unbiased Deep Solvers for Linear Parametric PDEs ⋮ Analysis on block chain financial transaction under artificial neural network of deep learning ⋮ On a Neural Network to Extract Implied Information from American Options ⋮ Meshless methods for American option pricing through physics-informed neural networks ⋮ Applications of artificial neural networks to simulating Lévy processes ⋮ Explainable neural network for pricing and universal static hedging of contingent claims ⋮ Smiles \& smirks: volatility and leverage by jumps ⋮ Precise option pricing by the COS method -- how to choose the truncation range ⋮ Artificial neural network for option pricing with and without asymptotic correction

Uses Software

• Adam

Cites Work

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• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• A Stochastic Approximation Method

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