Relationship between Least Squares Monte Carlo and Approximate Linear Programming

Operations Research Letters, 45, 5, 409-414, 2017

Posted: 28 Sep 2015 Last revised: 21 Sep 2018

See all articles by Selvaprabu Nadarajah

Selvaprabu Nadarajah

University of Illinois at Chicago - College of Business Administration

Nicola Secomandi

Carnegie Mellon University - David A. Tepper School of Business

Date Written: May 1, 2017

Abstract

Least squares Monte Carlo (LSM) is commonly used to manage and value early or multiple exercise financial or real options. Recent research in this area has started applying approximate linear programming (ALP) and its relaxations, which aim at addressing a possible ALP drawback. We show that regress-later LSM is itself an ALP relaxation that potentially corrects this ALP shortcoming. Our analysis consolidates two streams of research and supports using this LSM version rather than ALP on the considered models.

Keywords: Markov Decision Processes, Approximate Dynamic Programming, Least Squares Monte Carlo, Approximate Linear Programming, Financial and Real Options, Energy Storage

Suggested Citation

Nadarajah, Selvaprabu and Secomandi, Nicola, Relationship between Least Squares Monte Carlo and Approximate Linear Programming (May 1, 2017). Operations Research Letters, 45, 5, 409-414, 2017, Available at SSRN: https://ssrn.com/abstract=2666187 or http://dx.doi.org/10.2139/ssrn.2666187

Selvaprabu Nadarajah (Contact Author)

University of Illinois at Chicago - College of Business Administration ( email )

601 South Morgan Street
Chicago, IL 60607
United States

Nicola Secomandi

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

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