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This paper presents the benefits of using randomized neural networks instead of standard basis functions or deep neural networks to approximate the solutions of optimal stopping problems.
Option Pricing: A Simplified Approach
John C Cox, Stephen A Ross, and Mark Rubinstein · 1979
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Les Aspects Probabilistes du Controle Stochastique
N El Karoui · 1981
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Approximation Capabilities of Multilayer Feedforward Networks
Kurt Hornik · 1991
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A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
Steven L. Heston · 1993
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Numerical Valuation of High Dimensional Multivariate American Securities
Jérôme Barraquand and Didier Martineau · 1995
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Valuing American Options in a Path Simulation Model
James A. Tilley · 1995
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Neuro-Dynamic Programming
Dimitri P. Bertsekas and John N. Tsitsiklis · 1996
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Valuation of the Early-Exercise Price for Options using Simulations and Nonparametric Regression
Jacques F. Carriere · 1996
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Reinforcement Learning: A Survey
Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore · 1996
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Optimal Stopping, Free Boundary, and American Option in a Jump-Diffusion Model
Huyên Pham · 1997
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Optimal Stopping of Markov Processes: Hilbert Space Theory, Approximation Algorithms, and an Application to Pricing High-Dimensional Financial Derivatives
John Tsitsiklis and Benjamin Van Roy · 1997
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A Simple Approach to the Pricing of Bermudan Swaptions in the Multi-Factor Libor Market Model
Leif Andersen · 1999
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An Analysis of the Longstaff-Schwartz Algorithm for American Option Pricing
Emmanuelle Clément, Damien Lamberton, and Philip Protter · 2001
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Valuing American Options by Simulation: A Simple Least-Squares Approach
Francis A Longstaff and Eduardo S Schwartz · 2001
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Regression Methods for Pricing Complex American-Style Options
John Tsitsiklis and Benjamin Van Roy · 2001
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Monte Carlo Valuation of American Options
Chris Rogers · 2002
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On Bermudan Options
Martin Schweizer · 2002
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A Quantization Algorithm for Solving Multi-Dimensional Discrete-Time Optimal Stopping Problems
Vlad Bally and Gilles Pagès · 2003
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An Improved Simulation Method for Pricing High-Dimensional American Derivatives
Phelim P. Boyle, Adam W. Kolkiewicz, and Ken Seng Tan · 2003
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Convergence and Biases of Monte Carlo Estimates of American Option Prices using a Parametric Exercise Rule
Diego Garcia · 2003
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A Stochastic Mesh Method for Pricing High-Dimensional American Options
Mark Broadie and Paul Glasserman · 2004
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Pricing American Options: A Duality Approach
Martin B. Haugh and Leonid Kogan · 2004
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Iterative Construction of the Optimal Bermudan Stopping Time
Anastasia Kolodko and John Schoenmakers · 2004
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Convergence of the Least Squares Monte Carlo Approach to American Option Valuation
Lars Stentoft · 2004
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A Quantization Tree Method for Pricing and Hedging Multidimensional American Options
Vlad Bally, Gilles Pagès, and Jacques Printems · 2005
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Monte Carlo Algorithms for Optimal Stopping and Statistical Learning
Daniel Egloff · 2005
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A Regression-Based Monte Carlo Method to Solve Backward Stochastic Differential Equations
Emmanuel Gobet, Jean-Philippe Lemor, and Xavier Warin · 2005
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Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Guang-Bin Huang, Lei Chen, Chee Kheong Siew, et al · 2006
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Recurrent Neural Networks are Universal Approximators
Anton Maximilian Schäfer and Hans Georg Zimmermann · 2006
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A Dynamic Look-Ahead Monte Carlo Algorithm for Pricing Bermudan Options
A Review on Neural Networks with Random Weights
Weipeng Cao, Xizhao Wang, Zhong Ming, and Jinzhu Gao · 2018
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Perfect Hedging in Rough Heston Models
Omar El Euch and Mathieu Rosenbaum · 2018
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The Microstructural Foundations of Leverage Effect and Rough Volatility
Omar El Euch, Masaaki Fukasawa, and Mathieu Rosenbaum · 2018
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Volatility is Rough
Jim Gatheral, Thibault Jaisson, and Mathieu Rosenbaum · 2018
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Rough Volatility: Evidence from Option Prices
Giulia Livieri, Saad Mouti, Andrea Pallavicini, and Mathieu Rosenbaum · 2018
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Numerical Probability: An Introduction with Applications to Finance
Gilles Pagès · 2018
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Daniel Egloff, Michael Kohler, and Nebojsa Todorovic · 2007
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An Overview of Reservoir Computing: Theory, Applications and Implementations
Benjamin Schrauwen, David Verstraeten, and Jan Van Campenhout · 2007
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An Experimental Unification of Reservoir Computing Methods
David Verstraeten, Benjamin Schrauwen, Michiel d’Haene, and Dirk Stroobandt · 2007
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Q-Learning Algorithms for Optimal Stopping Based on Least Squares
Huizhen Yu and Dimitri P Bertsekas · 2007
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Representation of Measures with Polynomial Denseness in L p ( ℝ , d μ ) {L}_{p}(\mathbb{R},d\mu) , 0 < p < ∞ 0<p<\infty , and its Application to Determinate Moment Problems
Andrew Bakan · 2008
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Learning Exercise Policies for American Options
Yuxi Li, Csaba Szepesvari, and Dale Schuurmans · 2009
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Reservoir Computing Approaches to Recurrent Neural Network Training
Mantas Lukoševičius and Herbert Jaeger · 2009
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Convergence of a Least-Squares Monte Carlo Algorithm for American Option Pricing with Dependent Sample Data
Daniel Z Zanger · 2018
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Multifactor Approximation of Rough Volatility Models
Eduardo Abi Jaber and Omar El Euch · 2019
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Deep Optimal Stopping
Sebastian Becker, Patrick Cheridito, and Arnulf Jentzen · 2019
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Roughening Heston
Omar El Euch, Jim Gatheral, and Mathieu Rosenbaum · 2019
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American-Type Basket Option Pricing: A Simple Two-Dimensional Partial Differential Equation
Hamza Hanbali and Daniel Linders · 2019
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Pricing and Hedging American-Style Options with Deep Learning
Sebastian Becker, Patrick Cheridito, and Arnulf Jentzen · 2020
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Zap Q-Learning for Optimal Stopping
Shuhang Chen, Adithya M Devraj, Ana Bušić, and Sean Meyn · 2020
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The Quadratic Rough Heston Model and the Joint S&P 500/VIX Smile Calibration Problem
Jim Gatheral, Paul Jusselin, and Mathieu Rosenbaum · 2020
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Reservoir Computing Universality With Stochastic Inputs
L. Gonon and J. Ortega · 2020
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Estimating Full Lipschitz Constants of Deep Neural Networks
Calypso Herrera, Florian Krach, and Josef Teichmann · 2020
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General Error Estimates for the Longstaff–Schwartz Least-Squares Monte Carlo Algorithm
Daniel Z Zanger · 2020
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Neural Network Regression for Bermudan Option Pricing
Bernard Lapeyre and Jérôme Lelong · 2021
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American Options in the Volterra Heston Model
Etienne Chevalier, Sergio Pulido, and Elizabeth Zúñiga · 2022
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The Derivatives Academy
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Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats
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Simulated Greeks for American Options
Pascal Letourneau and Lars Stentoft · 2023
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