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Hedging retail promotions
(2012)
The combined impact of changing global demand and supply dynamics, extensive trading and speculation as well as global recessionary fears, has led to an environment of unprecedented volatility in worldwide commodity markets. As a result, effective risk management has become an increasingly important topic on the agenda of top management in a broad range of industries. While practical evidence shows that successful firms integrate both operational decision making and financial hedging in a firm-wide, coordinated risk management strategy, this entails managerial challenges. On the one hand, quantifying a firm’s exposure to raw material cost risk necessitates a sound understanding of the stochastic commodity market dynamics. On the other hand, once the exposure to different sources of risk is understood, executives face an intricate optimization problem over their operational and financial decision variables with the ultimate goal to reduce profit variability, while maintaining attractive business opportunities.
In this thesis, the topic of operational and financial risk management is investigated from three different perspectives.
In Chapter 2, a four-factor maximal affine stochastic volatility model of commodity prices is developed, which is consistent with many stylized characteristics of storable commodity markets as well as the historical term structure of commodity futures and option prices. Based on this model, we provide new insights with respect to the structural dynamics of commodity markets and the pricing and hedging of commodity derivatives. As the stochastic model used to describe the uncertain evolution of commodity prices can have important implications also in the valuation and risk management of real assets, a realistic commodity price model is a prerequisite for the integrated risk management models outlined in the subsequent chapters of this dissertation.
Given the previously developed intuition for commodity market dynamics, we model the integrated operational and financial risk management problem of a stylized, single-product industrial firm in Chapter 3. The firm faces risk in the price of commodity inputs and price sensitive, stochastic demand. Within this setting, the firm seeks to maximize inter-temporal utility under downside risk aversion over a multi-period time horizon by dynamically choosing physical procurement volumes, unit selling prices, and a futures hedge. We provide a flexible, simulation-based optimization algorithm, which allows us to solve the firm’s decision problem under realistic, multi-factor commodity price dynamics involving uncertainty in the interest rate and convenience yield as well as stochastic volatility. Based on this model, we characterize the firm’s optimal operating policy and investigate a range of topics including: (a) the value of managerial flexibility and the economic cost of restrictive supply contracts; (b) the importance of accounting for the stochastic nature of costs, interest rates, convenience yields, and volatility in risk management; (c) parameter and estimation risk; (d) the impact of risk aversion and hedging on the distribution of cash flows; and (e) the sensitivity of expected performance to key input parameters.
As opposed to the case of a single-product firm, integrated procurement risk management in a large, multi-divisional organization does not only require the above mentioned cross-functional coordination between, for example, the purchasing, sales, and finance department but also involves a cross-divisional coordination of actions in order to effectively target the firm-wide net risk exposure. To capture the specific aspects of integrated risk management in this type of setting, we extend the above model to a two-product firm in Chapter 4. Within this model, each of the two divisions are subject to cost and demand risk, which can be respectively correlated. Moreover, we allow for dynamic cross-selling to capture the potential complementarity/substitutability of items. The firm has access to futures, call, and put options associated with each of the commodity input markets for financial hedging. Under an intertemporal mean-variance utility function, we are able to provide analytic solutions to the firm’s dynamic procurement, pricing, and financial hedging problem. Based on a complementary numerical study, we analyze the impact of risk correlations and unilateral changes in the market environment of one division on the entire firm. Moreover, we discuss the impact of hedging and risk aversion on optimal policy and assess the effectiveness of different operational and financial hedging strategies for risk reduction.
This dissertation integrates three papers on risk management in complex new product development projects (NPD) with an integrated perspective on the automotive industry. It seeks to provide a comprehensive access to the topic by highlighting the most relevant risk management aspects in NPD – processes and methods (chapter 2), organization and coordination (chapter 3) as well as culture and strategy (chapter 4).
The focus of chapter 2 lies on the identification and assessment of risks as the initial and most critical risk management steps. It illustrates the application of the analytic hierarchy process (AHP) method as an established multi-criteria decision analysis method to create transparency on the overall risk position of a selected highly complex NPD in the automotive industry. Chapter 2 confirms the AHP method as a suitable approach for stronger preventive risk management in complex NPD with stakeholders with conflicting functional perspectives. This sets the stage for chapter 3 and a subsequent consideration of organization and coordination.
Chapter 3 considers an issue of organization and coordination in complex NPD that is mainly based on the fact that the involved stakeholders have individual targets and rely on concurrent processes. The selected automotive industry example illustrates the resulting managerial challenge of balancing the overall NPD requirements such as product characteristics and development lead time (system perspective) and the targets of single stakeholders in NPD (individual perspective). The NK model as an important setting for search builds the methodical basis to solve this issue and results in optimized organizational and coordination setups.
The implications on risk management in complex NPD as a result of newly emerging NPD types in the automotive industry are subject to chapter 4. These implications are in line with profound industry changes that include changes in the requirements of customers and markets. Chapter 4 uses complexity theory and deploys the lens of complex adaptive systems (CAS) to NPD to outline the emerging NPD types and their implications on risk framing activities. An extended risk framework is derived as basis that addresses the cultural and strategic shift in the automotive industry with regard to managing risk in emerging NPD types.