Risk Hedging for Production Planning

Wang, Liao; Yao, David D.

doi:10.1111/poms.13103

Cited by 10 publications

(4 citation statements)

References 46 publications

(66 reference statements)

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“…In other words, the solution to the base model induces an extremely risky payoff. That the newsvendor's maximum profit is very risky is also noted by Wang and Yao (2019), but their production model involves only production decisions while treating the pricing level as a given. We propose a risk-hedging model that improves the efficient frontier via substantial risk reductions from the base model.…”

Section: Base Model: Price-setting Newsvendormentioning

confidence: 97%

How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model

Wang¹,

Ye²,

Zhang³

2022

Preprint

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Financial asset price movement impacts product demand and thus influences operational decisions of a firm. We develop and solve a general model that integrates financial risk hedging into a price-setting newsvendor. The optimal hedging strategy is found analytically, which leads to an explicit objective function for optimization of pricing and service levels. We find that, in general, the presence of hedging reduces the optimal price. It also reduces the optimal service level when the asset price trend positively impacts product demand ("asset price benefits demand"), while it may increase the optimal service level by a small margin when the impact is negative ("asset price hurts demand"). We construct the mean-variance efficient frontier that characterizes the risk-return trade-off, and we quantify the risk reduction achieved by the hedging strategy. Our numerical case study using real data of Ford Motor Company shows that the markdown in price and decrease in service level are small under our model, and the hedging strategy substantially reduces risk without materially reducing operational profit.

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Section: Base Model: Price-setting Newsvendormentioning

confidence: 97%

How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model

Wang¹,

Ye²,

Zhang³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…A second direction is to modify one or more model ingredients and examine their effect on the results we obtained. These may include: a) The class of admissible hedges: they may range from American-style to path-dependent payoff schedule; b) Naked position revenues π: they may comprise newsvendor networks (Van Mieghem 2007), decentralized supply chains (Turcic et al 2015), and trading networks (Nadarajah and Secomandi 2018), among others; c) The target utility: risk aversion may be modeled by using risk-adjusted performance measures other than a MV criterion: they include exponential utility (Chen et al 2007), mean-CVaR (Conditional Value-at-Risk) criterion (Zhao and Huchzermeier 2017), expected shortfall (Wang and Yao 2019), and an upper bound constraining an assigned risk measure (Park et al 2017), among others.…”

Section: Future Developmentsmentioning

confidence: 99%

Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management

Guiotto

Roncoroni

2022

Operations Research

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Optimal Design of Combined Contingent Claims: Theory and Applications. In “Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management”, Paolo Guiotto and Andrea Roncoroni develop a normative framework for the optimal design, value assessment, and operations management integration of financial derivatives. Most business and operating revenues entail a mix of financially insurable and noninsurable risk. A risk-averse firm may face them by positioning in a pair of financial derivatives with optimal bespoke payoff functions; one claim is written on the insurable term, and the other claim is written on any observable index exhibiting correlation to the noninsurable term. On a theoretical ground, the authors 1) state the problem in a general setup and prove existence and uniqueness of the optimal pair of combined claims, 2) show that the optimal payoff functions satisfy a Fredholm integral equation, and 3) assess the incremental benefit the firm obtains by switching from the optimal single-claim custom hedge to the optimal combined custom hedge they propose. On an experimental ground, they show that 1) the optimal combined custom hedge would be empirically relevant for a highly risk-averse firm facing a market shock shown during the first period of the COVID-19 pandemic in 2020, 2) integration with the optimal procurement in a generalized newsvendor model leads to a significant improvement in both risk and return, and: 3) this gain can be traded off for a substantial enhancement in operational flexibility.

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“…Sun, Chung, and Ma (2020) study the application of data analytics to mitigate operational risk in the airline industry. Wang and Yao (2019) use data analytics to investigate how to jointly optimize the capacity decision and hedging decision. Ivanov, Dolgui, and Sokolov (2018) discuss the relationship between data analytics and supply chain disruption risk management.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*

et al. 2020

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In this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t-copula. Moreover, we develop a three-step importance sampling (IS) method for the t-copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg-Marquardt algorithm to estimate the mean-shift vector of the systematic risk factors after the probability measure change. Besides, we empirically * The authors would like to thank editors and three referees for valuable suggestions and helpful comments.

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Risk Hedging for Production Planning

Cited by 10 publications

References 46 publications

How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model

How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model

Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management

A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*

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