Production Planning with Risk Hedging under a CVaR Objective

40 Pages Posted: 8 Jul 2021 Last revised: 9 Jul 2021

Date Written: April 7, 2020

Abstract

A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral objective (such as the expected payoff), and allows for explicit modeling of the degree of aversion to downside risk (associated with low demand). The starting point of our model is to incorporate the impact on demand from a financial asset (including, for instance, a tradable market index as a proxy for the general economy) via a demand rate function. This way, in addition to the capacity decision at the beginning of the planning horizon, there is also a dynamic hedging strategy throughout the horizon, the latter plays the role of both mitigating demand risk and supplementing the payoff. The hedging strategy is restricted to partial information along with a cap on loss (pathwise). To find the optimal hedging strategy, we construct and solve a dual problem to derive the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via the martingale representation theorem. With the hedging strategy optimized, we show that optimizing the production quantity is a concave maximization problem. With both production and hedging (jointly) optimized, we provide a complete characterization of the efficient frontier, and quantify the improvement over the production-only approach. Furthermore, via sensitivity and asymptotic analyses, we spell out the impacts of the hedging budget and the risk aversion level, along with other qualitative insights.

Keywords: production planning, CVaR, downside risk aversion, optimal hedging

Suggested Citation

Wang, Liao and Yao, David, Production Planning with Risk Hedging under a CVaR Objective (April 7, 2020). Available at SSRN: https://ssrn.com/abstract=3875464 or http://dx.doi.org/10.2139/ssrn.3875464

Liao Wang (Contact Author)

HKU ( email )

Meng Wah Complex
Pokfulam Road
Hong Kong
China

David Yao

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

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