Deep Learning for Constrained Utility Maximisation
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- Lukas Gonon & Johannes Muhle-Karbe & Xiaofei Shi, 2019. "Asset Pricing with General Transaction Costs: Theory and Numerics," Papers 1905.05027, arXiv.org, revised Apr 2020.
- Philipp Grohs & Fabian Hornung & Arnulf Jentzen & Philippe von Wurstemberger, 2018. "A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations," Papers 1809.02362, arXiv.org, revised Jan 2023.
- Jan De Spiegeleer & Dilip B. Madan & Sofie Reyners & Wim Schoutens, 2018. "Machine learning for quantitative finance: fast derivative pricing, hedging and fitting," Quantitative Finance, Taylor & Francis Journals, vol. 18(10), pages 1635-1643, October.
- Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019.
"Pricing Options and Computing Implied Volatilities using Neural Networks,"
Risks, MDPI, vol. 7(1), pages 1-22, February.
- Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing options and computing implied volatilities using neural networks," Papers 1901.08943, arXiv.org, revised Apr 2019.
- Ma, Jingtang & Li, Wenyuan & Zheng, Harry, 2020. "Dual control Monte-Carlo method for tight bounds of value function under Heston stochastic volatility model," European Journal of Operational Research, Elsevier, vol. 280(2), pages 428-440.
- Stephan Eckstein & Michael Kupper, 2018. "Computation of optimal transport and related hedging problems via penalization and neural networks," Papers 1802.08539, arXiv.org, revised Jan 2019.
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Cited by:
- Kristof Wiedermann, 2022. "An SMP-Based Algorithm for Solving the Constrained Utility Maximization Problem via Deep Learning," Papers 2202.07771, arXiv.org.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-09-14 (Big Data)
- NEP-CMP-2020-09-14 (Computational Economics)
- NEP-UPT-2020-09-14 (Utility Models and Prospect Theory)
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