Optimal Control and Hedging of Operations in the Presence of Financial Markets

Caldentey, René; Haugh, Martin B.

doi:10.1287/moor.1050.0179

Cited by 105 publications

(35 citation statements)

References 23 publications

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“…Gaur and Seshadri (2005) consider a riskaverse newsvendor that hedges its risk exposure with financial options and show that financial hedging results in a larger order quantity. Caldentey and Haugh (2006) extend the work of Gaur and Seshadri by allowing continuous trading in the financial market. Chen et al (2007) incorporate risk aversion and financial hedging in multiperiod inventory and pricing models.…”

Section: Relation To the Literaturementioning

confidence: 82%

Operational Flexibility and Financial Hedging: Complements or Substitutes?

2010

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W e consider a firm that invests in capacity under demand uncertainty and thus faces two related but distinct types of risk: mismatch between capacity and demand and profit variability. Whereas mismatch risk can be mitigated with greater operational flexibility, profit variability can be reduced through financial hedging. We show that the relationship between these two risk mitigating strategies depends on the type of flexibility: Product flexibility and financial hedging tend to be complements (substitutes)-i.e., product flexibility tends to increase (decrease) the value of financial hedging, and, vice versa, financial hedging tends to increase (decrease) the value of product flexibility-when product demands are positively (negatively) correlated. In contrast to product flexibility, postponement flexibility is a substitute to financial hedging as intuitively expected. Although our analytical results assume perfect flexibility and perfect hedging and rely on a linear approximation of the value of hedging, we validate their robustness in an extensive numerical study.

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Section: Relation To the Literaturementioning

confidence: 82%

Operational Flexibility and Financial Hedging: Complements or Substitutes?

2010

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“…We approximate R via an analytic formula which is based on approximating the market price of risk by its initial value and using Theorem 2 in [2]. (We conjecture that an analytical formula could also be obtained for the exact value of R in our model.)…”

Section: Implementation Of the Quadratic Hedging Strategies And Numermentioning

confidence: 99%

Mean-variance hedging with oil futures

Wang

Wissel

2013

Finance Stoch

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We analyze mean-variance-optimal dynamic hedging strategies in oil futures for oil producers and consumers. In a model for the oil spot and futures market with Gaussian convenience yield curves and a stochastic market price of risk, we find analytical solutions for the optimal trading strategies. An implementation of our strategies in an out-of-sample test on market data shows that the hedging strategies improve long-term return-risk profiles of both the producer and the consumer.

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“…For example, Caldentey and Haugh (2005), and study models where the demand forecast is correlated with economic indices. Thus, a firm may profitably use a postponement strategy even when early demand cannot be observed.…”

Section: Introductionmentioning

confidence: 99%

“…One paradigm is based on the work of Sandmo (1971), and treats the decision-maker as risk-averse with an increasing concave or a mean-variance utility function. The operations literature that follows this preference-based approach in decision-making and hedging contexts includes Caldentey and Haugh (2005), Dong and Liu (2005), , Levy (1985), Mathur and Ritchken (1999), Perrakis and Ryan (1984), Ritchken (1985), Ritchken andKuo (1989), andVan Mieghem (2003). The other paradigm is based on Constantinides (1978) and McDonald and Siegel (1985), and assumes that the firm is owned by risk-averse investors, so that the decision maker must use the appropriate risk-adjusted cost of capital to make optimal investment decisions.…”

Section: Introductionmentioning

confidence: 99%

Optimal Timing of Inventory Decisions with Price Uncertainty

et al. 2015

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What is the optimal timing of inventory investment for a firm when its forecasts of demand and price improve with time but are correlated with the prices of traded assets in the financial markets? We consider this problem using a single period inventory model where demand is realized at time T and the stocking decision may be made at any time in the interval [0, T ]. The demand forecast evolves as a geometric Brownian motion, while the price forecase exhibits mean-reversion. The processes for the firm's value as well as that of the market evolve as geometric Brownian motions. We show that the right to make the optimal inventory decision is a modified American-style option. However, since the stochastic variables defining the forecasts of demand and price are not tradeable, we use a risk-adjusted valuation approach for incomplete markets to determine the optimal timing strategy.We derive the value of the inventory postponement option and the optimal exercise decision, that is, we present and analyze conditions under which early exercise of the inventory option takes place, and those under which postponement occurs. We also derive the comparative statics of optimal timing and stocking decisions relative to the key parameters: the market price of risk (and the cost of capital), the volatilities of price and demand, the correlation between these two variables and the return on the market portfolio, and the procurement cost.We illustrate the empirical validity of our model by testing it on firms in the gold mining industry. We show that our model performs well in explaining the variation in firm inventory as well as gross profit, and provides new evidence on the impact of price uncertainty on these variables.

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Optimal Control and Hedging of Operations in the Presence of Financial Markets

Cited by 105 publications

References 23 publications

Operational Flexibility and Financial Hedging: Complements or Substitutes?

Operational Flexibility and Financial Hedging: Complements or Substitutes?

Mean-variance hedging with oil futures

Optimal Timing of Inventory Decisions with Price Uncertainty

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