Exotic electricity options and the valuation of electricity generation and transmission assets
Decision Support Systems
Short-Term Generation Asset Valuation: A Real Options Approach
Operations Research
Short-Term Variations and Long-Term Dynamics in Commodity Prices
Management Science
Valuation of Commodity-Based Swing Options
Management Science
Soybean Inventory and Forward Curve Dynamics
Management Science
The fortune at the bottom of the pyramid
The fortune at the bottom of the pyramid
A Framework Using Two-Factor Price Lattices for Generation Asset Valuation
Operations Research
An Optimal Approximate Dynamic Programming Algorithm for the Lagged Asset Acquisition Problem
Mathematics of Operations Research
Optimal Commodity Trading with a Capacitated Storage Asset
Management Science
On the Pricing of Natural Gas Pipeline Capacity
Manufacturing & Service Operations Management
Information Relaxations and Duality in Stochastic Dynamic Programs
Operations Research
Optimal Control and Equilibrium Behavior of Production-Inventory Systems
Management Science
Valuation of Storage at a Liquefied Natural Gas Terminal
Operations Research
Managing Storable Commodity Risks: The Role of Inventory and Financial Hedge
Manufacturing & Service Operations Management
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We consider the integrated optimization problem of procurement, processing, and trade of commodities in a multiperiod setting. Motivated by the operations of a prominent commodity processing firm, we model a firm that procures an input commodity and has processing capacity to convert the input into a processed commodity. The processed commodity is sold using forward contracts, while the input itself can be traded at the end of the horizon. We solve this problem optimally and derive closed-form expressions for the marginal value of input and output inventory. We find that the optimal procurement and processing decisions are governed by price-dependent inventory thresholds. We use commodity markets data for the soybean complex to conduct numerical studies and find that approximating the joint price processes of multiple output commodities using a single, composite output product and using the approximate price process to determine procurement and processing decisions is near optimal. Compared to a myopic spread-option-based heuristic, the optimization-based dynamic programming policy provides significant benefits under conditions of tight processing capacities and high price volatilities. Finally, we propose an approximation procedure to compute heuristic policies and an upper bound to compare the heuristic against, when commodity prices follow multifactor processes.