Hedging Levels Research Articles

Production Control with Price, Cost, and Demand Uncertainty

An optimal production flow control problem of a make-to-stock manufacturing firm with price, cost, and demand uncertainty is studied. The objective of the flow rate control problem is maximizing the average profit that is the difference between the expected revenue and the expected production, inventory holding, and backlog costs. The uncertainties in the system are captured jointly in discrete environment states. In each environment state, the price, cost, and demand take different levels. The transitions between different environment states evolve according to a time-homogenous Markov chain. By using a continuous flow model, the optimal production policy is stated as a state-dependent hedging policy. The performance of the system where the production cost alternates between a high and a low cost level and the demand is either constant or also alternates between a high and a low level is analyzed under the double-hedging policy. According to this policy, the producer produces only when the cost is low and the surplus is between the two hedging levels. However when the backlog is below the lower hedging level, the producer produces with the maximum capacity regardless of the cost. The effects of production cost, production capacity, demand variability, and the dependence of the demand and the cost on the performance of the system are analyzed analytically and numerically. It is shown that controlling the production rate optimally allows producers respond to the fluctuations in price, cost, and demand in an effective way and maximize their profits.

A dynamic stochastic programming model for international portfolio management

We develop a multi-stage stochastic programming model for international portfolio management in a dynamic setting. We model uncertainty in asset prices and exchange rates in terms of scenario trees that reflect the empirical distributions implied by market data. The model takes a holistic view of the problem. It considers portfolio rebalancing decisions over multiple periods in accordance with the contingencies of the scenario tree. The solution jointly determines capital allocations to international markets, the selection of assets within each market, and appropriate currency hedging levels. We investigate the performance of alternative hedging strategies through extensive numerical tests with real market data. We show that appropriate selection of currency forward contracts materially reduces risk in international portfolios. We further find that multi-stage models consistently outperform single-stage models. Our results demonstrate that the stochastic programming framework provides a flexible and effective decision support tool for international portfolio management.

Hedging for multi-period downside risk in the presence of jump dynamics and conditional heteroskedasticity

This study extends the one period zero-VaR (Value-at-Risk) hedge ratio proposed by Hung et al . (2005) to the multi-period case and incorporates the hedging horizon into the objective function under VaR framework. The multi-period zero-VaR hedge ratio has several advantages. First, compared to existing hedge ratios based on downside risk, it has an analytical solution and is simple to calculate. Second, compared to the traditional Minimum Variance (MV) hedge ratio, it considers expected return and remains optimal while the Martingale process is invalid. Thirdly, hedgers may elect an adequate hedging horizon and confidence level to reflect their level of risk aversion using the concept of VaR. Pondering the occurrence of volatility clustering and price jumps, this study utilizes the ARJI model to compute time-varying hedge ratios. Finally, both in-sample and out-of-sample hedging effectiveness between one-period hedge ratio and multi-period hedge ratio are evaluated for four hedging horizons and various levels of risk aversion. The empirical results indicate that hedgers wishing to hedge downside risk over long horizons should use the multi-period zero-VaR hedge ratios.

A two-level hedging point policy for controlling a manufacturing system with time-delay, demand uncertainty and extra capacity

This paper focuses on the production control of a manufacturing system with time-delay, demand uncertainty and extra capacity. Time-delay is a typical feature of networked manufacturing systems (NMS), because an NMS is composed of many manufacturing systems with transportation channels among them and the transportation of materials needs time. Besides this, for a manufacturing system in an NMS, the uncertainty of the demand from its downstream manufacturing system is considered; and it is assumed that there exist two-levels of demand rates, i.e., the normal one and the higher one, and that the time between the switching of demand rates are exponentially distributed. To avoid the backlog of demands, it is also assumed that extra production capacity can be used when the work-in-process (WIP) cannot buffer the high-level demands rate. For such a manufacturing system with time-delay, demand uncertainty and extra capacity, the mathematical model for its production control problem is established, with the objective of minimizing the mean costs for WIP inventory and occupation of extra production capacity. To solve the problem, a two-level hedging point policy is proposed. By analyzing the probability distribution of system states, optimal values of the two hedging levels are obtained. Finally, numerical experiments are done to verify the effectiveness of the control policy and the optimality of the hedging levels.

Capacity and Production Managment in a Single Product Manufacturing System

In planning and managing production systems, manufacturers have two main strategies for responding to uncertainty: they build inventory to hedge against periods in which the production capacity is not sufficient to satisfy demand, or they temporarily increase the production capacity by “purchasing” extra capacity. We consider the problem of minimizing the long-run average cost of holding inventory and/or purchasing extra capacity for a single facility producing a single part-type and assume that the driving uncertainty is demand fluctuation. We show that the optimal production policy is of a hedging point policy type where two hedging levels are associated with each discrete state of the system: a positive hedging level (inventory target) and a negative one (backlog level below which extra capacity should be purchased). We establish some ordering of the hedging levels, derive equations satisfied by the steady-state probability distribution of the inventory/backlog, and give a more detailed analysis of the optimal control policy in a two state (high and low demand rate) model.

Production control of a pull system with production and demand uncertainty

We consider a continuous material-flow manufacturing system with an unreliable production system and a variable demand source which switches randomly between zero and a maximum level. The failure and repair times of the production system and the switching times of the demand source are assumed to be exponentially distributed random variables. The optimal production flow control policy that minimizes the expected average inventory carrying and backlog costs is characterized as a double-hedging policy. The optimal hedging levels are determined analytically by minimizing the closed-form expression of the cost function. We investigate two approximate single hedging policies. It is empirically shown that an approximate policy that uses a single hedging level which is the sum of a production uncertainty term and a demand uncertainty term gives accurate results for the expected average cost.

Implications of Crop Yield and Revenue Insurance for Producer Hedging

New types of crop insurance have expanded the tools from which crop producers may choose to manage risk. Little is known regarding how these products interact with futures and options. This analysis examines optimal futures and put ratios in the presence of four alternative insurance coverages. An analytical model investigates the comparative statics of the relationship between hedging and insurance. Additional numerical analysis is conducted which incorporates futures price, basis, and yield variability. Yield insurance is found to have a positive effect on hedging levels. Revenue insurance tends to result in slightly lower hedging demand than would occur given the same level of yield insurance coverage.

Forward Marketing Practices and Attitudes of Large-Scale Midwestern Grain Producers

Participants in the 1993, 1994, and 1995 Top Farmer Crop Workshops were surveyed with respect to their preharvest forward pricing, marketing practices, and marketing attitudes. Preharvest marketing of corn and soybeans, as of July 15 each year, was lower than hedging levels suggested by marketing research. Given the differences in price outlook for the years considered, producer behavior appears to reflect both long-term portfolio and short-term price enhancement effects. Producers' perceptions of the effectiveness of various marketing methods for price enhancement and price protection contrasted with theoretical characteristics. Views about futures and options contracts were generally similar for users and nonusers. It was found that self-assessed marketing skills, use of written marketing plans, and use of marketing consultants had little effect on marketing performance. These results, together with other recent studies, are used for suggestions about the design of future marketing educational programs.

Scheduling of an unreliable manufacturing system with nonresumable setups

This paper considers the scheduling of a manufacturing system with nonresumable setup changes. The system considered involves an unreliable machine that can produce two part types. The switchover from one part type to the other incurs a given constant setup time. The setups are nonresumable, i.e. after a machine repair completion, a setup decision has to be made. The parts have specified constant processing time and constant demand rate. We give a continuous dynamic programming formulation of the problem, which is solved numerically. The optimal setup switching policies are shown to be hedging corridors. Two heuristics, for the determination of the hedging levels, are provided. We show, through simulation, that the two heuristics exhibit good performance.