designers or theorists, weprovide proveable results that incomplete information about the state of the systemhinders computation. Although we model only one “simple” random price movement, the EUR/USD exchange rate, our results demonstrate that currrency hedgingin practice may be a hard problem from a computational complexity perspectiveand that optimal solutions to real-world hedging problems can be approximated atbest. We demonstrate through extensive numerical tests the viability of a simulation/optimization model as a decision support tool for foreign exchange management. We find in our experiments that scatter search is a search method that is bothaggressive and robust. It is aggressive because it finds high-quality solutions early inthe search. It is robust because it continues to improve upon the best solution whenallowed to search longer. Our approach to hedging foreign exchange transaction riskis based on exchange rate expectations, considers real market data and incorporatesflexible weights. We find that the approach adds value in terms of reducing riskand enhancing income. The optimized mean-variance-skewness strategy providessuperior risk-return results in comparison to the passive strategies if earnings riskis perceived asymmetrically in terms of downside risk. Conditioning informationtherefore seems to be important. Even with low levels of predictability, there is asubstantial loss in opportunity when fixed-weight strategies (which assume no predictability) are implemented relative to the dynamic strategy that incorporates conditioning information. The pure forward strategy is found to have the lowest return perunit of earnings risk whereas the straddle strategy and the 1/3 strategy reveal similarrisk-return characteristics. Interestingly, our research also contrasts the finding thatcurrency forward contracts generally yield better results in comparison to optionssince a passive straddle strategy would have yielded superior results compared to aforward strategy. Our results are as follows. Apart from our backtesting results, it isbelieved that the proposed simulation/optimization procedure for determining optimal solutions has important implications for policy making. Having easy access torelevant solutions makes it easier for policy makers to explore and experiment withthe model while incorporating their own intuition before deciding on a final plan ofaction. Despite the many problems that economic forecasts from economic systems
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17 Exchange Rate Hedging in a Simulation/Optimization Framework
confront, these models offer a vehicle for understanding and learning from failures,as well as consolidating our growing knowledge of economic behavior.Future research: The only way to guarantee the best possible combination is toknow in advance whether the currency will move in one’s favor or not. Hence, therewill always be a natural desire to foresee the future by improving on forecasting.This may require more complex models than our “simple” stochastic representationof the exchange rate. Where our model only describes statistically some restrictedaspects of this rich complexity, real-world complexity is associated with a range ofbehaviors and functions that go way beyond our approximation. Thus, it can be argued that more complex behavior needs more complex models. For instance, oneimportant additional element adding to real-world complexity is that risk management objectives are often perceived towards different reference levels, such as exposure, target exchange rates as established from their own budgeting and planningprocesses, or even personal anchor points that may involve expert forecasts. Second, hedging policies are often benchmark-orientated, position adjustment is donein terms of market values, and exposures are uncertain. Third, a hedger’s perceptionof risk may depend on different states of nature, which makes it a multidimensional phenomenon. These considerations call for a more in-depth model of hedging, which would allow the hedge ratios to have a stochastic nature. Another, morepopular field of research, involves modeling departures from the normal distribution of exchange rate returns in order to better capture the probability of tail events.Throughout Part IV, we assumed that the individual utility function is known andgiven as an easily applicable formula. We therefore ignored the difficulty of learningone’s utility function which may consitute another interesting real-world problem.
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