We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. mlOSP presents a unified numerical implementation of Regression Monte Carlo (RMC) approaches to optimal stopping, providing a state-of-the-art, open-source, reproducible and transparent platform. Highlighting its modular nature, we present multiple novel variants of RMC algorithms, especially in terms of constructing simulation designs for training the regressors, as well as in terms of machine learning regression modules. The latter include Gaussian Process regression and neural networks. Furthermore, mlOSP nests most of the existing RMC schemes, allowing for a consistent and verifiable benchmarking of extant algorithms. In turn, using Optimal Stopping as a starting point for more complex problems, we present extensions of mlOSP to price swing options and to solve a stochastic impulse control case study.

Bio: Mike Ludkovski is a Professor of Statistics and Applied Probability at University of California Santa Barbara where he co-directs the Center for Financial Mathematics and Actuarial Research. Among his research interests are Monte Carlo techniques for optimal stopping/stochastic control, stochastic modeling of energy markets, and applications of machine learning in longevity and non-life insurance. His research has been supported by NSF, DOE, ARPA-E and CAS. He holds a Ph.D. in Operations Research and Financial Engineering from Princeton University and has held visiting positions at London School of Economics and Paris Dauphine University.