Least Squares Monte Carlo and Approximate Linear Programming with an Energy Real Option Application

Least squares Monte Carlo (LSM) is an approximate dynamic programming technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. We provide numerical backing for the usefulness of this solution using a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our findings motivate additional research to obtain even better methods than the regress-later version of LSM.