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We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems.
Solving high-dimensional optimal stopping problems using deep learning
S. Becker, P. Cheridito, A. Jentzen, and T. Welti · 1908
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Valuing American options by simulations: a simple least squares approach
F. Longstaff and E. Schwartz · 2001
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Regression methods for pricing complex American-style options
J. Tsitsiklis and B. Van Roy · 2001
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A primal-dual simulation algorithm for pricing multi-dimensional American options
L. Andersen and M. Broadie · 2004
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A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options
D. Egloff, M. Kohler, and N. Todorovic · 2007
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Improved lower and upper bound algorithms for pricing American options by simulation
M. Broadie and M. Cao · 2008
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Optimal multiple stopping and valuation of swing options
R. Carmona and N. Touzi · 2008
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A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time
M. Kohler · 2008
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Stochastic kriging for simulation metamodeling
B. Ankenman, B. L. Nelson, and J. Staum · 2010
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A review on regression-based Monte Carlo methods for pricing American options
M. Kohler · 2010
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Pricing of high-dimensional American options by neural networks
M. Kohler, A. Krzyżak, and N. Todorovic · 2010
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Sequential Monte Carlo pricing of American-style options under stochastic volatility models
B. R. Rambharat and A. E. Brockwell · 2010
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Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates
D. Belomestny · 2011
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Monte-Carlo valorisation of American options: facts and new algorithms to improve existing methods
B. Bouchard and X. Warin · 2011
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Pricing of American options in discrete time using least squares estimates with complexity penalties
M. Kohler and A. Krzyżak · 2012
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Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by kriging-based metamodeling and optimization
O. Roustant, D. Ginsbourger, Y. Deville, et al · 2012
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LSMonteCarlo: American options pricing with Least Squares Monte Carlo method , 2013
M. A. Beketov · 2013
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Kriging metamodels and experimental design for Bermudan option pricing
M. Ludkovski · 2018
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rlsm: R package for least squares Monte Carlo
J. Yee · 2018
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Machine learning for pricing American options in high dimension
L. Goudenege, A. Molent, A. Zanette, et al · 2019
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J. Lelong · 2019
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Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions
Y. Chen and J. W. Wan · 2020
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Practical heteroscedastic Gaussian process modeling for large simulation experiments
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STochastic OPTimization library in C++ , 2018
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Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models
L. Goudenège, A. Molent, and A. Zanette · 2020
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mlOSP: Regression Monte Carlo Algorithms for Optimal Stopping , 2020
M. Ludkovski · 2020
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Adaptive batching for Gaussian process surrogates with application in noisy level set estimation
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Optimal stopping via randomized neural networks
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Regression Monte Carlo for impulse control
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Neural optimal stopping boundary
A. M. Reppen, H. M. Soner, and V. Tissot-Daguette · 2022
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