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Author: Mingwei Liao Publisher: Open Dissertation Press ISBN: 9781361355602 Category : Languages : en Pages :
Book Description
This dissertation, "Futures Hedging on Both Procurement Risk and Sales Risk Under Correlated Prices and Demand" by Mingwei, Liao, 廖明瑋, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The profitability of a manufacturer could be largely affected by underlying uncertainties embedded in the fast-changing business environment. Random factors, such as input material price at the procurement end or output product price and demand at the sales end, might produce significant risks. Effective financial hedging therefore needs to be taken to mitigate these risk exposures. Although it is common to use commodity futures to control the risks at either end separately, little has been done on the hedging of these risk exposures in an integrated manner. Therefore, this study aims to develop a planning approach that performs financial hedging on both the procurement risk and the sales risk in a joint manner. This planning approach is based on a framework that has a risk-averse commodity processor that procures input commodity and sells output commodity in the spot market, while hedging the procurement risk and sales risk through trading futures contracts in the commodity markets. Both the input and output commodities futures are used for the hedging. A both-end-hedging model is developed to quantitatively evaluate the approach. The evaluation is based on an objective function that considers both profit maximisation and risk mitigation. Decisions on spot procurement, input futures hedging position, and output futures hedging position are optimised simultaneously. As the input commodity is the main production material for the output commodity, positive correlation between the input material price and the output product price is considered. The customer demand is considered negatively correlated with the output product price. An ethanol plant using corn as the main input material is employed as an example to implement the proposed model. The model is represented as a stochastic program, and the Gibson-Schwartz two-factor model is employed to describe the stochastic commodity prices. Historical commodity price data are used to estimate the parameters for the two-factor model with state-space form and Kalman filter. By generating various scenarios representing evolving prices and the random customer demand, the stochastic program could be solved using linear programming algorithms under its deterministic equivalent. Numerical experiments are carried out to demonstrate the benefit that could be gained from applying the both-end-hedging approach proposed in this study. Comparing with traditional no-hedging model or single-end-hedging models, the improvement obtained from the proposed model is found to be significant. The effectiveness of the model is further tested in various price trend and price correlation, demand elasticity and volatility, and risk attitude of the decision maker. It is found that the proposed approach is robust in these various circumstances, and the approach is especially effective when the price trend is uncertain and when the decision maker has a strong risk-averse attitude. DOI: 10.5353/th_b5270559 Subjects: Industrial procurement - Planning Risk management - Mathematical models Sales management
Author: Mingwei Liao Publisher: Open Dissertation Press ISBN: 9781361355602 Category : Languages : en Pages :
Book Description
This dissertation, "Futures Hedging on Both Procurement Risk and Sales Risk Under Correlated Prices and Demand" by Mingwei, Liao, 廖明瑋, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The profitability of a manufacturer could be largely affected by underlying uncertainties embedded in the fast-changing business environment. Random factors, such as input material price at the procurement end or output product price and demand at the sales end, might produce significant risks. Effective financial hedging therefore needs to be taken to mitigate these risk exposures. Although it is common to use commodity futures to control the risks at either end separately, little has been done on the hedging of these risk exposures in an integrated manner. Therefore, this study aims to develop a planning approach that performs financial hedging on both the procurement risk and the sales risk in a joint manner. This planning approach is based on a framework that has a risk-averse commodity processor that procures input commodity and sells output commodity in the spot market, while hedging the procurement risk and sales risk through trading futures contracts in the commodity markets. Both the input and output commodities futures are used for the hedging. A both-end-hedging model is developed to quantitatively evaluate the approach. The evaluation is based on an objective function that considers both profit maximisation and risk mitigation. Decisions on spot procurement, input futures hedging position, and output futures hedging position are optimised simultaneously. As the input commodity is the main production material for the output commodity, positive correlation between the input material price and the output product price is considered. The customer demand is considered negatively correlated with the output product price. An ethanol plant using corn as the main input material is employed as an example to implement the proposed model. The model is represented as a stochastic program, and the Gibson-Schwartz two-factor model is employed to describe the stochastic commodity prices. Historical commodity price data are used to estimate the parameters for the two-factor model with state-space form and Kalman filter. By generating various scenarios representing evolving prices and the random customer demand, the stochastic program could be solved using linear programming algorithms under its deterministic equivalent. Numerical experiments are carried out to demonstrate the benefit that could be gained from applying the both-end-hedging approach proposed in this study. Comparing with traditional no-hedging model or single-end-hedging models, the improvement obtained from the proposed model is found to be significant. The effectiveness of the model is further tested in various price trend and price correlation, demand elasticity and volatility, and risk attitude of the decision maker. It is found that the proposed approach is robust in these various circumstances, and the approach is especially effective when the price trend is uncertain and when the decision maker has a strong risk-averse attitude. DOI: 10.5353/th_b5270559 Subjects: Industrial procurement - Planning Risk management - Mathematical models Sales management
Author: Yihua Xu Publisher: Open Dissertation Press ISBN: 9781361476444 Category : Languages : en Pages :
Book Description
This dissertation, "Procurement Risk Management Using Commodity Futures: a Multistage Stochastic Programming Approach" by Yihua, Xu, 許意華, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: ABSTRACT This study addresses the procurement risks that arise from variations in customer demand and fluctuations in the prices of material to be purchased, and seeks ways to effectively manage these risks. Procurement is prone to risks due to the uncertainties in, for example, demand, price and delivery. The effective management of these risks is hence a critical provision within the framework of procurement planning. However, what generally interests a procurement manager, when attempting to match closely product supply with customer demand, is the lowest cost that could possibly be attained. This mindset is found to concur with traditional models for procurement planning, which tend also to focus on cost minimization or the maximization of profit. With the potential risks largely ignored, such traditional models are clearly inadequate in the dynamic and precarious environment in which procurement is to be performed. This study describes a procurement planning approach that takes into account the risks arising from the fluctuations in procurement prices and customer demand volatility during a procurement undertaking. From the perspective of risk management, procurement is concerned with minimizing the downside risk exposure by means of hedging the associated risks so as to avoid possible losses. The specific risk hedging method developed in this study is based on the commodities and derivatives markets, which have grown rapidly and flourished in the age of e-commerce. This method is based on the static financial risk-hedging models that deal with a fixed hedged quantity. However, in making operational decisions in which the purchased quantity fluctuates due to customer demand, hedging has to be performed dynamically and this forms a significant extension to the available models. To allow and support operational procurement decision making as well as financial risk hedging in the presence of commodity markets, an integrated procurement risk management framework is developed. The development of this framework involves three major research issues (i) the establishment of a quantitative procurement risk management framework; (ii) the modelling of the stochastic behaviour of commodity prices and customer demand; and (iii) in II matching the two stochastic quantities mentioned above, the modelling of the procurement planning and financial risk hedging problem, jointly represented as a multistage stochastic program. The solutions obtained from this stochastic programming model can be evaluated according to the specified profit/risk profiles of a decision maker. To model the stochastic behaviour of commodity prices, the Gibson-Schwartz two-factor model and the Schwartz-Smith two-factor model are employed for storable commodities and non-storable commodities respectively. State-space form models and Kalman filtering are used to estimate the parameters of the empirical price models based on historical commodity price data. Two commodities are studied in this research. One is copper which is storable, and the other is electricity which is non-storable. Using the empirical price models, scenarios can be generated for stochastic program optimization. Numerical experiments are carried out to demonstrate the benefit that could be gained from the use of the integrated procurement risk management approach developed in this study. It is found that, when compared with pure operational pla
Author: Ankur Goel Publisher: ISBN: Category : Languages : en Pages :
Book Description
We consider a firm that procures an input commodity to produce an output commodity to sell to the end retailer. 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 either through a spot or a forward contract. Input and output commodity prices are also correlated and follow a joint stochastic price process. The firm maximizes the value of its stakeholders by jointly determining the optimal procurement policy of the input commodity, and the financial hedging policy for the sales of the output commodity. We show that partial hedging dominates both perfect hedging and no-hedging when input price, output price, and demand are correlated, and we characterize this optimal hedging policy. We identify sufficient conditions under which myopic policy is optimal for the price taking firm. We also show that expected base-stock policy is optimal in the presence of yield uncertainty. 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 the futures price data between 4/1/2005 to 12/31/2011 from Chicago Board of Trade (CBOT) for corn and ethanol.
Author: Li Shi Publisher: Open Dissertation Press ISBN: 9781361310021 Category : Languages : en Pages :
Book Description
This dissertation, "Long-term Commodity Procurement Risk Management Using Futures Contracts: a Dynamic Stack-and-roll Approach" by Li, Shi, 时莉, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The procurement of commodity materials for production is an important issue in supply chain management. Effective procurement should consider both uncertain customer demand and fluctuating commodity price which, when act together, give rise to the procurement risk. To protect the bottom line, a manufacturer has to plan its procurement activities with special attention given to such procurement risk. Existing research has studied the use of exchange market-traded commodities in mitigating procurement risk. This study addresses the case of a manufacturer with long-term procurement commitments who wishes to hedge against the risk exposure by using long-dated futures contracts. In the commodities markets, however, long-dated futures are often illiquid or even unavailable, thus making the hedge ineffective. Alternatively, in a stack-and-roll hedge, the hedging positions are rolled forward in actively traded short-dated futures contracts of equal maturity until the procurement is executed. This in effect replicates the long-term futures contract in performing a hedge. This study therefore aims at developing a dynamic stack-and-roll approach that can effectively manage the long maturity procurement risk. The proposed dynamic stack-and-roll approach is inherently a discrete-time hedging strategy that divides the procurement planning horizon into multiple decision stages. The nearby futures are adopted as the short-dated futures as they are typically liquid. The hedging positions are adjusted periodically in response to the commodity price behaviour and updated information about the forward customer demand. For a manufacturer who wishes to mitigate the procurement risk as well as maximise the terminal revenue after the procurement, the mean-variance objective function is employed to model the manufacturer's risk aversion behaviour. Then, a dynamic program formulation of the approach is presented for determining a closed-form expression of the optimal hedging positions. Notice that the hedging policy is a time-consistent mean-variance policy in discrete-time, in contrast to the existing discrete hedging approaches that employ minimum-variance policies. In this study, the commodity prices are modelled by a fractal nonlinear regression process that employs a recurrent wavelet neural network as the nonlinear function. The purpose of this arrangement is to incorporate the fractal properties discovered in commodity prices series. In the wavelet transform domain, fractal self-similarity and self-affinity information of the price series over a certain time scale can be extracted. The Extended Kalman Filter (EKF) algorithm is applied to train the neural network for its lower training error comparing with classical gradient descent algorithms. Monthly returns and volatility of commodity prices are estimated by daily returns data in order to increase the estimation accuracy and facilitate effective hedging. The demand information is updated stage by stage using Bayesian inference. The updating process are defined and adapted to a filtration, which can be regarded as the information received at the beginning of each decision stage. Numerical experiments are carried out to evaluate the performance of the proposed stack-and-roll approach. The results show that the proposed approach robustly outperforms other hedging strategies that employ minimum-variance or nai
Author: Su Dai Publisher: GRIN Verlag ISBN: 3656539219 Category : Business & Economics Languages : en Pages : 80
Book Description
Master's Thesis from the year 2013 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,7, University of Mannheim, language: English, abstract: The commodity futures contract is an agreement to deliver a specific amount of commodity at a future time . There are usually choices of deliverable grades, delivery locations and delivery dates. Hedging belongs to one of the fundamental functions of futures market. Futures can be used to help producers and buyers protect themselves from price risk arising from many factors. For instance, in crude oil commodities, price risk occurs due to disrupted oil supply as a consequence of political issues, increasing of demand in emerging markets, turnaround in energy policy from the fossil fuel to the solar and efficient energy, etc. By hedging with futures, producers and users can set the prices they will receive or pay within a fixed range. A hedger takes a short position if he/she sells futures contracts while owning the underlying commodity to be delivered; a long position if he/she purchases futures contracts. The commonly known basis is defined as the difference between the futures and spot prices, which is mostly time-varying and mean-reverting. Due to such basis risk, a naïve hedging (equal and opposite) is unlikely to be effective. With the popularity of commodity futures, how to determine and implement the optimal hedging strategy has become an important issue in the field of risk management. Hedging strategies have been intensively studied since the 1960s. One of the most popular approaches to hedging is to quantify risk as variance, known as minimum-variance (MV) hedging. This hedging strategy is based on Markowitz portfolio theory, resting on the result that “a weighted portfolio of two assets will have a variance lower than the weighted average variance of the two individual assets, as long as the two assets are not perfectly and positively correlated.” MV strategy is quite well accepted, however, it ignores the expected return of the hedged portfolio and the risk preference of investors. Other hedging models with different objective functions have been studied intensively in hedging literature. Due to the conceptual simplicity, the value at risk (VaR) and conditional value at risk (C)VaR have been adopted as the hedging risk objective function. [...]
Author: Qiang Li Publisher: ISBN: 9781361033739 Category : Languages : en Pages :
Book Description
This dissertation, "Integrating Commodity Futures in Procurement Planning and Contract Design With Demand Forecast Update" by Qiang, Li, 李強, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: This study aims at investigating the benefits of integrating commodity futures contracts in devising commodity procurement policies as well as the design of supply contracts. To achieve this, a two-tier decentralised supply chain with uncoordinated risk transfer behaviours is studied. Specifically, the supply chain consists of a risk-neutral manufacturer (he) and a risk-averse retailer (she), where both players maximise their own objective functions by utilising the demand forecast update over the planning horizon. The mean-variance utility is employed to capture the retailer''s risk aversion behaviour. For the first objective, this study considers a commodity procurement problem for the risk-neutral manufacturer. It shows that partially procuring in the forward market is potentially beneficial because the logistics costs tend to be larger for tighter delivery schedule and vice versa. Existing literature has studied the value of forward procurement. This study further explores the value of the dynamic adjustment in the forward (futures) market in response to the demand information update. Specifically, when the joint distribution of demand and new information is a bivariate normal distribution, the optimal procurement policy is characterized analytically. The second objective is studied within the supply chain setting, where the manufacturer is assumed to be the Stackelberg leader. Recently, various financial hedging strategies have been developed to mitigate the price risks for firms which directly procure commodities for their operations. However, few, if any, studies have addressed the integration of financial hedging with supply contract design so that the risk exposure faced by the downstream player in the supply chain could be partially hedged. Although the downstream retailer does not procure any commodity directly, she may suffer from the commodity price volatility propagated from the upstream manufacturer. By formulating the problem as a dynamic program, a flexible contract with time-consistent closed-form financial hedging policy is derived. Numerical experiments are carried out to demonstrate the benefits gained by integrating the commodity futures contract with supply chain decision making. In the implementation, the short-term/long-term model developed by Schwartz and Smith is adopted to describe the stochastic behaviour of the price. Moreover, to preclude any risk-free arbitrage opportunity, the risk-neutral version of the model is employed. To take full advantage of the historical commodity price data, the smoother-based approach, rather than filter-based approach, is adopted to estimate the latent parameters of the stochastic price processes. For the manufacturer, it is shown that the value of the futures market is significant in the presence of logistics cost. Moreover, extra value could be obtained by adjusting the position in futures contracts in response to the newly observed information. For the decentralised supply chain, compared with the wholesale price contract, it is shown that the proposed flexible contract could improve the performance of the supply chain by leading to higher payoffs for both firms. Furthermore, the results show that flexible contract with financial hedging is effective on mitigating the commodity price risk exposure transferred from the manufacturer to the retailer when measured by standard deviation (SD), value-a
Author: Panos Kouvelis Publisher: John Wiley & Sons ISBN: 1118115791 Category : Business & Economics Languages : en Pages : 497
Book Description
A comprehensive, one-stop reference for cutting-edge research in integrated risk management, modern applications, and best practices In the field of business, the ever-growing dependency on global supply chains has created new challenges that traditional risk management must be equipped to handle. Handbook of Integrated Risk Management in Global Supply Chains uses a multi-disciplinary approach to present an effective way to manage complex, diverse, and interconnected global supply chain risks. Contributions from leading academics and researchers provide an action-based framework that captures real issues, implementation challenges, and concepts emerging from industry studies.The handbook is divided into five parts: Foundations and Overview introduces risk management and discusses the impact of supply chain disruptions on corporate performance Integrated Risk Management: Operations and Finance Interface explores the joint use of operational and financial hedging of commodity price uncertainties Supply Chain Finance discusses financing alternatives and the role of financial services in procurement contracts; inventory management and capital structure; and bank financing of inventories Operational Risk Management Strategies outlines supply risks and challenges in decentralized supply chains, such as competition and misalignment of incentives between buyers and suppliers Industrial Applications presents examples and case studies that showcase the discussed methodologies Each topic's presentation includes an introduction, key theories, formulas, and applications. Discussions conclude with a summary of the main concepts, a real-world example, and professional insights into common challenges and best practices. Handbook of Integrated Risk Management in Global Supply Chains is an essential reference for academics and practitioners in the areas of supply chain management, global logistics, management science, and industrial engineering who gather, analyze, and draw results from data. The handbook is also a suitable supplement for operations research, risk management, and financial engineering courses at the upper-undergraduate and graduate levels.
Author: Leonardo Martinez-Diaz Publisher: U.S. Commodity Futures Trading Commission ISBN: 057874841X Category : Science Languages : en Pages : 196
Book Description
This publication serves as a roadmap for exploring and managing climate risk in the U.S. financial system. It is the first major climate publication by a U.S. financial regulator. The central message is that U.S. financial regulators must recognize that climate change poses serious emerging risks to the U.S. financial system, and they should move urgently and decisively to measure, understand, and address these risks. Achieving this goal calls for strengthening regulators’ capabilities, expertise, and data and tools to better monitor, analyze, and quantify climate risks. It calls for working closely with the private sector to ensure that financial institutions and market participants do the same. And it calls for policy and regulatory choices that are flexible, open-ended, and adaptable to new information about climate change and its risks, based on close and iterative dialogue with the private sector. At the same time, the financial community should not simply be reactive—it should provide solutions. Regulators should recognize that the financial system can itself be a catalyst for investments that accelerate economic resilience and the transition to a net-zero emissions economy. Financial innovations, in the form of new financial products, services, and technologies, can help the U.S. economy better manage climate risk and help channel more capital into technologies essential for the transition. https://doi.org/10.5281/zenodo.5247742
Author: Mingwei Liao
Author: Mingwei Liao
Author: 廖明瑋
Author: Yihua Xu
Author: Ankur Goel
Author: Li Shi
Author: Su Dai
Author: Qiang Li
Author: Panos Kouvelis
Author: Robert C. Anderson
Author: Leonardo Martinez-Diaz