Deep combinatorial optimisation for optimal stopping time problems : application to swing options pricing
This work addresses a computational bottleneck in financial engineering by enabling efficient pricing of complex options, though it is incremental as it builds on existing neural network and randomization techniques.
The authors tackled the problem of pricing high-dimensional American and swing options by proposing a neural network-based method for stochastic control that models policies directly, avoiding dynamic programming or backward stochastic differential equations. The algorithm successfully priced these options in reasonable computation time, which classical methods cannot achieve for high dimensions.
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.