We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. Least squares Monte Carlo combined with information relaxation and duality is a state-of-the-art reinforcement learning methodology to obtain operating...

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...

Least squares Monte Carlo (LSM) is a state-of-the-art approximate dynamic programming approach used in financial engineering and real options to value and manage options with early or multiple exercise opportunities. It is also applicable to capacity investment and inventory/production...

We formulate the merchant trading of energy in a network of storage and transport assets as a Markov decision process with uncertain energy prices, generalizing known models. Because of the intractability of our model, we develop heuristics and both lower and dual (upper) bounds on the optimal...

The real option management of commodity conversion assets gives rise to intractable Markov decision processes (MDPs), in part due to the use of high dimensional models of commodity forward curve evolution, as commonly done in practice. Focusing on commodity storage, we identify a deficiency of...

Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) 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...