Deep neural network expressivity for optimal stopping problems (Q6565562)

scientific article; zbMATH DE number 7874611

Language	Label	Description	Also known as
English	Deep neural network expressivity for optimal stopping problems	scientific article; zbMATH DE number 7874611

Statements

instance of

scholarly article

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title

Deep neural network expressivity for optimal stopping problems (English)

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author

Lukas Gonon

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published in

Finance and Stochastics

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publication date

2 July 2024

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zbMATH Keywords

deep neural network

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optimal stopping problem

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Markov processes

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MaRDI profile type

Publication

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cites work

Universal approximation bounds for superpositions of a sigmoidal function

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Randomized Optimal Stopping Algorithms and Their Convergence Analysis

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Optimal stopping with signatures

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Deep optimal stopping

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Solving high-dimensional optimal stopping problems using deep learning

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On the rates of convergence of simulation-based optimization algorithms for optimal stopping problems

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TRUE UPPER BOUNDS FOR BERMUDAN PRODUCTS VIA NON‐NESTED MONTE CARLO

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Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations

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An analysis of a least squares regression method for American option pricing

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Pricing Bermudan Options Using Regression Trees/Random Forests

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DNN expression rate analysis of high-dimensional PDEs: application to option pricing

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Convergence and biases of Monte Carlo estimates of American option prices using a parametric exercise rule

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Uniform error estimates for artificial neural network approximations for heat equations

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Deep ReLU network expression rates for option prices in high-dimensional, exponential Lévy models

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Deep ReLU neural networks overcome the curse of dimensionality for partial integrodifferential equations

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A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations

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Error bounds for approximations with deep ReLU neural networks in Ws,p norms

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Expressivity of Deep Neural Networks

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Pricing American Options: A Duality Approach

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Optimal Stopping via Randomized Neural Networks

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A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations

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The stochastic grid bundling method: efficient pricing of Bermudan options and their Greeks

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Proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients

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PRICING OF HIGH-DIMENSIONAL AMERICAN OPTIONS BY NEURAL NETWORKS

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Neural network regression for Bermudan option pricing

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Deep ReLU networks and high-order finite element methods

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Optimal approximation of piecewise smooth functions using deep ReLU neural networks

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Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems

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Monte Carlo valuation of American options

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Q4937701

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Provable approximation properties for deep neural networks

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DGM: a deep learning algorithm for solving partial differential equations

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Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus

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Deep learning of free boundary and Stefan problems

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Error bounds for approximations with deep ReLU networks

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Identifiers

DOI

10.1007/s00780-024-00538-0

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6565562