Hedging Production Risk With Options

We examine how corporations should choose their optimal mix of linear and nonlinear derivatives. We present a model in which a firm facing both quantity (output) and price (market) risk maximizes its expected profits when subjected to financial distress costs. The optimal hedging position generally is comprised of linear contracts, but as the levels of quantity and price-risk increase, the use of linear contracts will decline due to the risks associated with overhedging. At the same time, a substitution effect occursThe authors acknowledge the helpful comments and conversations we have had with Mike Rebello, Steve Smith, Alan Tucker, P. V. Viswanath, Jayant Kale, Tom Webster, and seminar participants at Pace University and the University of South Florida. They also are appreciative of the comments provided by an anonymous reviewer. Smithson (1998) for an excellent summary and also the seminal articles of DeMarzo and Duffie (1995), Froot, Scharfstein, and Stein (1993), and Smith and Stulz (1985. 2 A professional version of this article, with anecdotal and empirical support for our hypotheses, appears in Gay, Nam, and Turac (2002). toward the use of nonlinear contracts. The degree of substitution will depend on the correlation between output levels and prices. Our model also allows us to provide insight into the relation between a firm's derivatives usage and its transaction-cost structure.

Section: Prior Research On the Linear/ Nonlinear Instrument Choicementioning

confidence: 99%

On the optimal mix of corporate hedging instruments: Linear versus nonlinear derivatives

Nam

Turac

2003

“…If the model incorporates basis risk and options markets in a mean-variance framework, and if the options premiums and futures prices are unbiased, then options turn out to be redundant hedging instruments; the optimal hedging strategy involves only futures (Lapan, Moschini, & Hanson, 1991). If production risk is introduced into the model, options enter the portfolio (Sakong, Hayes, & Hallam, 1993).…”

Section: Conceptual Framework Of Optimal Portfoliosmentioning

confidence: 99%

Risk Management in Agricultural Markets: A Review

Tomek

Peterson

2001

This article surveys and evaluates the current state of knowledge about producers' marketing strategies to manage price and revenue risk for farm commodities. The review highlights gaps between concepts and their implementation. Many well-developed models of price behavior exist, but appropriate characterization and estimation of the probability distributions of commodity prices remain elusive. Hence, the preferred measure of price risk is ambiguous. Numerous models of optimal marketing portfolios for farmers have been specified, but their behavior appears to be inconsistent with most, if not all, of these models. In addition, some research suggests that farmers can earn speculative profits, which is inconsistent with notions of efficient markets. The conclusions discuss what academic research can and cannot accomplish in relation to assisting producers with risk-management decisions.

“…The specific constellation examined here refers to an example given by Sakong, Hayes, and Hallam (1993), who analyzed the hedging behavior of a corn producer in Iowa. In this…”

Section: Hedging Uncertain Cash Flows From Corn Production In Iowamentioning

confidence: 99%

“…For sales volumes not exceeding 40,000 bushels, a ϭ 0.0009 is chosen; for sales volumes ranging between 40,000 and 45,000 bushels (inclusive), a ϭ 0.00045 is chosen; and for those in excess of 45,000 bushels, a ϭ 0.00015 is chosen. Sakong et al (1993) constructed the utility function by presupposing for each subinterval an exponential utility function It is now possible to carry out a numerical examination of the scenarios. Sakong et al (1993) did so by applying a specific grid search.…”

Section: Hedging Uncertain Cash Flows From Corn Production In Iowamentioning

confidence: 99%

Hedging in Incomplete Markets: An Approximation Procedure for Practical Application

Breuer

Gürtler

2001

Derivative financial instruments are frequently used as a tool for influencing the risk of entrepreneurial uncertain payoff. To this end, an approximation procedure is developed capable of calculating the optimal quantity of derivatives to be used. It is assumed that the entrepreneurial cash flow is governed by several stochastic factors and that derivatives are only available as a hedging tool for one of these factors. In general, it is easy to determine optimal hedging payment structures with respect to this factor, but real-life hedging opportunities will typically not allow to perfectly reproduce such a fictitious payment structure, thus leading to complex numerical optimization problems. Instead of directly approximating the entrepreneurial expected utility maximum, we suggest using the fictitious optimal hedging payment structure as a starting point and to minimize the quadratic deviation between payment structures realizable by financial derivatives actually available and the resulting entrepreneurial payoff achieving the fictitious optimal hedging payment structure. This approach proves to be rather easy. Indeed, under certain conditions an explicit solution can be reached. After analyzing the qualitative properties of our approximation solution, we examine its efficiency for two practical hedging problems. In the first example, we get nearly the same solutions with our approximation procedure as with a grid programming approach presented by some other authors. Among other things, our second example may explain why some special kinds of financial derivatives, known as shared currency option under tenders, are not used in international invitations for tenders even though they offer hedging opportunities that are otherwise not available.