Corpus ID: 227239065

mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms

@article{Ludkovski2020mlOSPTA,
  title={mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms},
  author={M. Ludkovski},
  journal={arXiv: Computational Finance},
  year={2020}
}
  • M. Ludkovski
  • Published 2020
  • Computer Science, Economics, Mathematics
  • arXiv: Computational Finance
We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. The template is implemented in the R statistical environment and publicly available via a GitHub repository. 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… Expand
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SHOWING 1-10 OF 33 REFERENCES
Sequential Design for Optimal Stopping Problems
Solving high-dimensional optimal stopping problems using deep learning
STochastic OPTimization library in C
...
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3
4
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