A Stochastic Dominance Approach to Evaluating Foreign Exchange Hedging Strategies
Hunter, William C., Timme, Stephen G., Financial Management
William C. Hunter is Vice-President and Economist of the Federal Reserve Bank of Atlanta, and an Adjunct Professor of Finance at Emory University, Atlanta, Georgia. Stephen G. Timme is an Associate Professor of Finance at Georgia State University, Atlanta, Georgia.
Managers in multinational corporations, and many individuals, find themselves exposed to foreign exchange risk. Importers and exporters, for example, often need to make commitments to buy or sell goods for delivery at some future time, with the payment to be made in a foreign currency. Likewise, multinational corporations operating foreign subsidiaries receive payments from their subsidiaries that are denominated in a foreign currency. The transactions exposure, i.e., the risk associated with exchanging one currency for another, and the translations exposure -- the risk associated with expressing the value of one currency in terms of another, engendered by foreign currency transactions are prime examples of the significant risks that multinational corporations face in their foreign operations. Given these risks, it is clear that a properly designed and executed foreign exchange hedging strategy can play a significant role in determining the financial success of a firm's foreign operations.
The financial implications of decisions based on inaccurate or poorly designed foreign exchange hedging strategies, require that managers frequently evaluate alternative hedging strategies. In response to this need, previous studies have examined the ex-post performance of various foreign exchange hedging strategies. The approach used in these studies is to establish a criterion, i.e., some characteristic of a desirable hedging strategy, and then compare the results obtained using the various competing strategies relative to this criterion. The evaluation criteria are typically stated in terms of rate of return (relative to a never hedge or always hedge strategy), revenues, or profits.
It is clear from the foreign exchange hedging literature that different hedging strategies generally result in different distributions of outcomes (e.g., profits or rate of return). If the decision-maker is risk-averse or if the outcome distributions are normally distributed, then the familiar mean-variance criterion can be used to select the optimal hedging strategy. On the other hand, when the decisionmaker's utility function is not known or the outcome distributions are not normally distributed, the popular mean-variance criterion cannot always discern a unique choice. In these cases, evaluation criteria which are grounded in utility maximization, robust to nonnormal distributions, and capable of yielding optimal hedging strategies for entire classes of risk-averse or risk-neutral decision-makers are clearly desirable.
The evaluation criteria advocated in this paper emanate from standard stochastic dominance rules. It is shown that these criteria provide managers with a practical means for evaluating ex-post foreign exchange hedging strategies since they are applied to real business variables, e.g., profits, revenues, market values, or returns.(1) In addition, the criteria agree with the familiar mean-variance selection rule when the outcome distributions are normally distributed and the decision-maker is risk-averse. Thus, the approach offered in this paper represents an improvement over traditional hedging strategy evaluation methodologies.
In what follows, we quickly review some prominent criteria for ranking uncertain outcomes such as those resulting from employing different hedging strategies. Next is a brief presentation of stochastic dominance rules and how these rules can be used to form forecast evaluation criteria. This is followed by an example illustrating how the stochastic dominance criteria can be used to evaluate hedging strategies based on foreign exchange forecasts. The paper ends with a summary, conclusion, and suggestions for future research. …