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1、DecisionSupportSystems42(2006)1054–1062www.elsevier.com/locate/dswAhybridmodelforexchangeratepredictionHuseyinIncea,TheodoreB.Trafalisb,*aSchoolofBusinessAdministration,GebzeInstituteofTechnology,CayVrovaFab.YoluNo:101P.K:14141400,Gebze,Kocaeli,TurkeybSchoolofIndustrialEngineering,UniversityofOk
2、lahoma,202WestBoyd,Room124,Norman,OK73019,UnitedStatesReceived21July2004;receivedinrevisedform30August2005;accepted11September2005Availableonline20October2005AbstractExchangerateforecastingisanimportantproblem.Severalforecastingtechniqueshavebeenproposedinordertogainsomeadvantages.Mostofthemaree
3、itherasgoodasrandomwalkforecastingmodelsorslightlyworse.Someresearchersarguedthatthisshowstheefficiencyoftheexchangemarket.Weproposeatwostageforecastingmodelwhichincorporatesparametrictechniquessuchasautoregressiveintegratedmovingaverage(ARIMA),vectorautoregressive(VAR)andco-integrationtechni-qu
4、es,andnonparametrictechniquessuchassupportvectorregression(SVR)andartificialneuralnetworks(ANN).Comparisonofthesemodelsshowedthatinputselectionisveryimportant.Furthermore,ourfindingsshowthattheSVRtechniqueoutperformstheANNfortwoinputselectionmethods.D2005ElsevierB.V.Allrightsreserved.Keywords:Ex
5、changerateprediction;Neuralnetworks;Supportvectorregression;Timeseries1.Introductioncorrelationandnonlinearityintimeseries.Theyareinthefamilyofparametricmodels.EconometricandtimeExchangerateforecastingisanimportantproblemseriesmodelsarewidelyappliedtoforeignexchangethathasbeenstudiedbyresearcher
6、sandpractitionersmarket.Severalresearcherscriticizedtheforecastingextensively.Itisarguedthatexchangeratemarketisperformanceofthesetechniquesandsomeofthemveryefficient.Therefore,itisdifficulttomakeshorthavefoundthattherandomwalkmodeloutperformstermandlongtermforecastingefficiently[2,13,25,38].the
7、econometricandtimeseriestechniques[18,25,38].SeveraltechniqueshavebeenproposedandappliedtoThereasonisthatmostoftheeconometricmodelsareexchangerateforecastingandestimationofthevolatil-linearandusedunderspecificassumptions.For
