Dynamic Risk Management of Commodity Operations: Model and Analysis

We study the merits and limitations of technology improvement (TI) initiatives for managing input–price risk. Such initiatives (e.g., energy efficiency projects) typically reduce the consumption of an input commodity and so result in lower production costs, more sustainable operations, and/or an improved competitive position. This study explores whether TI can also serve to hedge risks. Although TI clearly reduces both average cost and risk exposure, some firms may actually benefit from input–price uncertainty; the result, when combined with production flexibility, is an “option value” that firms may well be reluctant to forgo. We develop a stylized mathematical model to examine the incentives of different types of firms to adopt TI. Thus, we derive a closed‐form expression that quantifies a firm’s attitude toward input–price risk by considering the firm’s certainty premium, or what the firm would pay to “lock in” the unit input price, and then link that premium to various firm‐ and industry‐specific characteristics. We also compare the risk management advantages of technology improvement vs. financial hedging (FH) and give conditions under which these strategies are complements or substitutes. Our results show that, even when input‐price uncertainty is desirable for firms, they can still benefit from investing in risk reduction measures—such as TI and FH—because the uncertainty’s option value could thereby increase. A firm’s ability to adjust its price in response to both market competition and input‐price variation mediates the benefit of risk‐reducing measures and also affects the complementarity of these two strategies.

Section: Literature Reviewmentioning

confidence: 92%

“…The main focus of this literature is on using financial instruments to reduce the “mismatch cost” between supply and demand (Devalkar et al. , Kouvelis et al. , Park et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Input‐Price Risk Management: Technology Improvement and Financial Hedging

Shantia

Aflaki

Ghoddusi

2019

“…2013, Devalkar et al. 2011, 2018, Felix and Weber 2012, Geman and Kourouvakalis 2008, Goel and Gutierrez 2011, Hahn and Dyer 2008, Jaillet et al. 2004, Kamrad and Ernst 2001, Lai et al.…”

Section: Solution Approaches and Implications Of Price Model Choicementioning

confidence: 99%

“…(2011), Devalkar et al. (2011, 2018), Goel and Gutierrez (2011), Goel and Tanrisever (2017), Kim and Powell (2011), Felix and Weber (2012), Secomandi and Wang (2012), Wu et al. (2012), Arvesen et al.…”

Section: Introductionmentioning

confidence: 99%

A Discussion of Non‐Gaussian Price Processes for Energy and Commodity Operations

Gambaro

Secomandi

2021

Energy sources and commodities exhibit high price risk. This risk is thus an important feature of operational models of the value chains for these goods. These models typically employ Gaussian‐based representations of the evolution of this uncertainty. This approach facilitates the optimization of operational policies but is at odds with empirical facts about energy and commodity prices, which are better captured by non‐Gaussian processes. We discuss this alternative modeling strategy, focusing on Lévy processes. As an illustration, we show that it substantially increases the optimal policy value in a simplified merchant natural gas storage setting. Further, we highlight potential implications of using this approach to formulate realistic energy and commodity operations models. Our work has broader relevance for modeling the dynamics of both other market variables and operational quantities, such as exchange rates and demand forecasts. The study of how the adoption of non‐Gaussian processes may impact energy and commodity operations is an appealing area for future research.

“…More recently, Devalkar et al. () consider commodity processing and risk management in partially complete markets in the presence of financial distress costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial Hedging and Optimal Procurement Policies under Correlated Price and Demand

Goel

Tanrısever

2017

W e consider a firm that procures an input commodity to produce an output commodity to sell to the end retailer. The retailer's demand for the output commodity is negatively correlated with the price of the output commodity. The firm can sell the output commodity to the retailer through a spot, forward or an index-based contract. Input and output commodity prices are also correlated and follow a joint stochastic price process. The firm maximizes shareholder value by jointly determining optimal procurement and hedging policies. We show that partial hedging dominates both perfect hedging and no-hedging when input price, output price, and demand are correlated. We characterize the optimal financial hedging and procurement policies as a function of the term structure of the commodity prices, the correlation between the input and output prices, and the firm's operating characteristics. In addition, our analysis illustrates that hedging is most beneficial when output price volatility is high and input price volatility is low. Our model is tested on futures price data for corn and ethanol from the Chicago Mercantile Exchange.