Deep combinatorial optimisation for optimal stopping time problems : application to swing options pricing

2020/01/30 by Thomas Deschatre, Deschatre, Thomas, Joseph Mikael +1
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Auction Theory and Applications #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2001.11247

openalex publication_date 2020/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A new method for stochastic control based on neural networks and using randomisation of discrete random variables is proposed and applied to optimal stopping time problems. The method models directly the policy and does not need the derivation of a dynamic programming principle nor a backward stochastic differential equation. Unlike continuous optimization where automatic differentiation is used directly, we propose a likelihood ratio method for gradient computation. Numerical tests are done on the pricing of American and swing options. The proposed algorithm succeeds in pricing high dimensional American and swing options in a reasonable computation time, which is not possible with classical algorithms.

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