Efficient methods for computing investment strategies for multi-market commodity trading

The focus of this work is the computation of efficient strategies for commo dity trading in a multimarket environment. In today's " global economy " co mmodities are often bought in one location and then sold (right away, or af ter some storage period) in different markets. Thus, a trading decision in one location must be based on expectations about future price curves in all other relevant markets, and on current and future storage and transportati on costs. Investors try to compute a strategy that maximizes expected retur n, usually with some limitations on assumed risk. With standard stochastic assumptions on commodity price fluctuations, computing an optimal strategy can be modeled as a Markov decision process (MDP). However, in general, suc h a formulation does not lead to efficient algorithms. In this work a model for representing the multi market trading problem is proposed and how to o btain efficient structured algorithms for computing optimal strategies is s hown for a number of commonly used trading objective functions (expected ne t present value, mean-variance, and value at risk).