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Quantitative Finance Volume 20, 2020 - Issue 4
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Research Papers

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

Ludovic GoudenègeFéderation de Mathématiques de CentraleSupélec – CNRS FR3487, Gif-sur-Yvette, FranceView further author information
,
Andrea MolentDipartimento di Scienze Economiche e Statistiche, Università degli Studi di Udine, Udine, ItalyView further author information
&
Antonino ZanetteDipartimento di Scienze Economiche e Statistiche, Università degli Studi di Udine, Udine, ItalyCorrespondence[email protected]
View further author information
Pages 573-591 | Received 25 Jun 2019, Accepted 29 Nov 2019, Published online: 29 Jan 2020

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Johan Auster, Ludovic Mathys & Fabio Maeder. (2022) JDOI variance reduction method and the pricing of American-style options. Quantitative Finance 22:4, pages 639-656.
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Peter K. Friz, Paul Gassiat & Paolo Pigato. (2022) Short-dated smile under rough volatility: asymptotics and numerics. Quantitative Finance 22:3, pages 463-480.
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Mike Ludkovski & Yuri Saporito. (2021) KrigHedge: Gaussian Process Surrogates for Delta Hedging. Applied Mathematical Finance 28:4, pages 330-360.
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Ruimeng Hu. (2020) Deep learning for ranking response surfaces with applications to optimal stopping problems. Quantitative Finance 20:9, pages 1567-1581.
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Endah R.M. Putri, Muhammad L. Shahab, Mohammad Iqbal, Imam Mukhlash, Amirul Hakam, Lutfi Mardianto & Hadi Susanto. (2024) A deep-genetic algorithm (deep-GA) approach for high-dimensional nonlinear parabolic partial differential equations. Computers & Mathematics with Applications 154, pages 120-127.
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Florian Bourgey, Stefano De Marco, Peter K. Friz & Paolo Pigato. (2023) Local volatility under rough volatility. Mathematical Finance.
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Bradley Sturt. (2023) A Nonparametric Algorithm for Optimal Stopping Based on Robust Optimization. Operations Research.
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Oleg Kudryavtsev & Natalia Danilova. (2023) APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS TO SIMULATING LÉVY PROCESSES. Journal of Mathematical Sciences 271:4, pages 421-433.
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M. Ludkovski. (2023) Statistical Machine Learning for Quantitative Finance. Annual Review of Statistics and Its Application 10:1, pages 271-295.
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Ludovic Goudenège, Andrea Molent & Antonino Zanette. (2022) Moving average options: Machine learning and Gauss-Hermite quadrature for a double non-Markovian problem. European Journal of Operational Research 303:2, pages 958-974.
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S. Yazdani, M. Hadizadeh & V. Fakoor. (2022) Computational analysis of the behavior of stochastic volatility models with financial applications. Journal of Computational and Applied Mathematics 411, pages 114258.
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Philipp Grohs, Arnulf Jentzen & Diyora Salimova. (2022) Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms. Partial Differential Equations and Applications 3:4.
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Leonardo Kanashiro Felizardo, Elia Matsumoto & Emilio Del-Moral-Hernandez. (2022) Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise. Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise.
Nan Li. (2022) An Iteration Algorithm for American Options Pricing Based on Reinforcement Learning. Symmetry 14:7, pages 1324.
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Etienne Chevalier, Sergio Pulido & Elizabeth Zún͂iga. (2022) American Options in the Volterra Heston Model. SIAM Journal on Financial Mathematics 13:2, pages 426-458.
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Christian Bayer, Jinniao Qiu & Yao Yao. (2022) Pricing Options under Rough Volatility with Backward SPDEs. SIAM Journal on Financial Mathematics 13:1, pages 179-212.
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Dennis Elbrächter, Philipp Grohs, Arnulf Jentzen & Christoph Schwab. (2021) DNN Expression Rate Analysis of High-Dimensional PDEs: Application to Option Pricing. Constructive Approximation 55:1, pages 3-71.
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Ludovic Goudenège, Andrea Molent & Antonino Zanette. (2020) Gaussian process regression for pricing variable annuities with stochastic volatility and interest rate. Decisions in Economics and Finance 44:1, pages 57-72.
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Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen & Ariel Neufeld. (2021) Deep Splitting Method for Parabolic PDEs. SIAM Journal on Scientific Computing 43:5, pages A3135-A3154.
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Yannick Dillschneider. (2022) A Machine Learning Framework for Asset Pricing. SSRN Electronic Journal.
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Jingtang Ma, Wensheng Yang & Zhenyu Cui. (2021) Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models. SSRN Electronic Journal.
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Johan Auster, Ludovic Mathys & Fabio Mäder. (2021) JDOI Variance Reduction Method and the Pricing of American-Style Options. SSRN Electronic Journal.
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Bradley Sturt. (2021) A nonparametric algorithm for optimal stopping based on robust optimization. SSRN Electronic Journal.
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Pietro Rossi, Flavio Cocco & Giacomo Bormetti. (2020) Deep Learning Profit & Loss. SSRN Electronic Journal.
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