The Role of Price Spreads and Reoptimization in the Real Option Management of Commodity Storage Assets

The real option management of commodity storage assets is an important practical problem. Practition- ers approach the resulting stochastic optimization model using heuristic policies that rely on sequential reoptimization of linear programs. Used in conjunction with Monte Carlo simulation, these policies typ- ically yield near optimal lower bound estimates on the value of storage. This paper reveals that a simple one stage lookahead policy is optimal for a fast storage asset without frictions. Thus, in this (not entirely realistic) case the problem is easy and the reoptimization policies are unnecessary, albeit optimal. In contrast, this paper provides numerical and structural justification for the use of these policies in the general case. Further, the use of price spreads simplifies the estimation of near tight dual upper bounds on the value of storage. This approach relies on using the fast and frictionless asset optimal value func- tion to estimate dual upper bounds in the general case. Monte Carlo simulation and linear programming thus appear adequate for the near optimal valuation and management of commodity storage assets.</p

Optimal Energy Procurement in Spot and Forward Markets

Storage capacity for energy, such as electricity, natural gas, and oil, is limited. Thus, spot and forward purchases for delivery on the usage date play an important role in matching the supply and the uncertain demand of energy. Transaction costs tend to be larger in spot than forward energy, and more generally commodity, markets. Hence, partially procuring supply in the forward market, rather than entirely in the spot market, is a potentially valuable real option. We call this option the forward procurement option. The study of this option from the perspective of differential transaction costs has received little attention in the literature. We thus formulate and analyze a parsimonious procurement model with differential spot and forward transaction costs and correlated spot demand and nominal price random variables. Our analysis, in part based on natural gas data, sheds novel light on the value of the forward procurement option and its optimal exercise, as well as their sensitivities to parameters of interest. Our main insight is that procuring the demand forecast in the forward market is nearly optimal on the instances that we consider. This greatly simplifies the management of this option. We obtain analogous results with a richer model in which the supply procured in the forward market is delivered at multiple dates. Beyond energy, our research has potential relevance for the procurement of other commodities, such as metals and agricultural products.</p

Commodity Procurement with Demand Forecast and Forward Price Updates

Commodities, ranging from natural gas to memory chips, can be procured both by trading on the date in spot markets and in advance in forward markets. Transaction costs, such as brokerage fees, are typically higher in spot markets than in forward markets. Moreover, the forecast of a ¯rm's commodity requirement (demand) for a given future date typically changes in an uncertain fashion over time. Thus, although the dynamics of forward and spot prices are notoriously uncertain, firms that procure commodities face the dilemma of choosing between early and possibly less expensive commitments with residual demand uncertainty and late and possibly more expensive sourcing of the exact amount needed. We investigate this issue by developing and analyzing a model of commodity procurement for a single future date. Our model generalizes models available in the real options and operations management literature, by simultaneously considering correlated demand forecast and forward price updates in a setting characterized by multiple forward transactions and a single spot transaction. We derive the structure of the optimal procurement policy and discuss its computation in cases of practical interest. In a numerical study, based on applying our model to natural gas data, we offer managerial insights on the effects that demand forecast and forward price updates, both in isolation and combined, have on the value of a firm's procurement policy. We also assess the sensitivities of these effects to parameters of interest and the potential managerial relevance of the combined effect. Our model and results have significance beyond the specific application

Revenue Management with Bargaining

Static game-theoretic models of bilateral bargaining assume that the seller knows his valuation for the item that is up for sale; that is, how the seller may determine this quantity is exogenous to these models. In this paper, we develop and analyze a stylized Markov decision process that endogenizes the seller's computation of his marginal inventory valuation in an infinite horizon revenue management setting when each sale occurs according to a given bilateral bargaining mechanism. We use this model to compare, both analytically and numerically, the seller's performance under four basic bilateral bargaining mechanisms with a tractable information structure. These comparisons provide insights on the seller's performance under the following trading arrangements: buyer and seller posted pricing, negotiated pricing, and rule based pricing

Computing the Value of the Real Option to Transport Natural Gas and Its Sensitivities

In the United States natural gas pipelines lease their transport capacity to shippers via contracts, which shippers manage as real options on differences between natural gas prices at different geographical locations. In practice it is common to value these real options using spread option valuation techniques, because they quickly compute both their values and, even more important, their sensitivities to parameters of interest (the greeks). Although fast, we show that in the general case of network contracts this approach is heuristic. Thus, we propose a novel and computationally efficient method that estimates the exact real option value of such a contract and computes unbiased estimates of its greeks, based on the application of linear programming, Monte Carlo simulation, and direct greek estimation techniques. We test this method on realistic instances modeled after contracts available on the Transco pipeline, using a reduced form model of the risk neutral evolution of natural gas prices calibrated on real data. Our main findings are that our method can significantly improve the practice based valuations of these contracts, by up to about 10%, and the application of direct greek estimation techniques is critical to make our method computationally efficient. Our work is relevant to natural gas shippers; a version of our model was recently implemented by a major international energy trading company. Potentially, our work has wider significance for the valuation and management of other commodity and energy real options, whose payoffs are determined by solving capacity constrained optimization models

An Analytical Approximation for the Throughput of a Closed Fork/Join Network with Multi-Station Input Subnetworks

Fork/join stations are used for modeling synchronization between entities, and fork/join queueing networks are natural models for a variety of communication and manufacturing systems: Parallel computer networks, fabrication/assembly systems, supply chains and material control strategies for manufacturing systems. Exact solutions of general fork/join networks can only be obtained by using numerical methods to analyze the underlying Markov chains. However, this method is computationally feasible only for networks with small population size and number of stations. In this paper, we present a simple approximation method to estimate the throughput of a closed queueing network that features a single fork/join station receiving inputs from multi-station subnetworks. Our technique uses aggregation to estimate the arrival process from input subnetworks. Given the estimated arrival process, we then derive closed form approximate expression for the network throughput by analyzing a simplified Markov chain. A numerical study shows that the proposed approximation is fairly effective, particularly for large network sizes

Interaction between Technology and Extraction Scaling Real Options in Natural Gas Production

This paper is the outcome of a research engagement studying questions of technology utilization and production management with managers at EQT Corp., an integrated natural gas production and distribution company. The question of how to best leverage the use of technology is fundamental to almost any industry; this is especially true for those companies operating in the volatile field of commodities production, as EQT Corp. does. We consider the interaction between two types of real options that arise in natural gas production: The option to scale the production level, through enhanced extraction and communication technologies, and the option to scale the extraction rate, by pausing production. We study this interaction by applying stochastic dynamic programming to actual operational and financial data. Our analysis brings to light data-driven managerial principles pertaining to the valuation and deployment of these three scaling options, the effect of price uncertainty on the option values, how to effectively simplify the optimal deployment policy, and whether these options, or subsets of them, are complements or substitutes. These principles are significant to natural gas production managers and, potentially, to managers of other natural resource production processes, such as the extraction of oil and mining

Technology Deployment Decisions in the Natural Gas Industry

Since natural gas reserves in North America and Europe are declining, energy companies there can no longer create value by just drilling more wells; they must create value by better managing their current production assets, and by better leveraging technology. In this paper, we analyze the real option to pause production from a well (better management) along with the real option to scale production technology (leverage technology), within a model incorporating price and geological uncertainty.We use our model to generate insights through numerical experiments, using actual financial and well data provided by Equitable Resources. We find, surprisingly, that the often neglected option to pause production adds up to 5% to the value of a well. Furthermore, we find that technology scaling options add up to 65% through enhanced extraction technology, and almost 5% through improved communication technology. That is, we show that profits can be increased by managing for value, and not just by drilling more wells. In addition, our work provides insights into the optimal tactical management of a well when adjusting to changes in price, production rate, and seasonal cost factors. Our results extend to other natural resource extraction projects, such as mining and oil extraction, and may extend to other production processes such as farming and forestry

An Integrated Framework for the Analysis of New Technology Selection for an Application to the LNG Industry

A fundamental issue in the management of technology innovation, both in manufacturing and service industries, is the comparative evaluation of emerging and incumbent technologies. This evaluation entails the juxtaposition of multiple aspects including process configuration and operational and financial performance. In this paper we present an integrated analytic framework for technology selection that models the relation between these three critical dimensions. We apply our framework in the context of the liqueed natural gas industry, in which new o shore vessel-based regasification technology has recently been developed as an alternative to conventional onshore terminal-based regasification. We analyze the impact of process configuration and operational and financial performance on technology selection, and identify the conditions under which a specific regasification technology and its configuration is appropriate for adoption. We also investigate how the insights we derive may depend on how one models stochastic variability in the relevant processing times

Real Option Management of Hydrocarbon Cracking Operations

Commodity conversion assets play important economic roles. It is well known that the market value of these assets can be maximized by managing them as real options on the prices of their inputs and/or outputs. In particular, when futures on these inputs and outputs are traded, managing such real options, that is, valuing, hedging, and exercising them, is analogous to managing options on such futures, using risk neutral valuation and delta hedging methods. This statement holds because dynamically trading portfolios of these futures and a risk less bond can replicate the cash °ows of these assets. This basic principle is not always appreciated by managers of commodity conversion assets. Moreover, determining the optimal operational cash °ows of such an asset requires optimizing the asset operating policy. This issue complicates the real option management of commodity conversion assets. This chapter illustrates the application of this approach to manage a hydrocarbon cracker, a specific commodity conversion asset, using linear programming and Monte Carlo simulation. The discussion is based on a simplified representation of the operations of this asset. However, the material presented here has potential applicability to the real option management of more realistic models of hydrocarbon cracking assets, as well as other energy and commodity conversion assets.</p