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1、ANINTELLIGENTASSETALLOCATIONANDPORTFOLIOOPTIMIZATION:NewApproachesforLongTermInvestingByUgurArslanUgur5261@gmail.comInternationalBusinessResearchConference,February9-11,2007CogginSchoolofBusiness,UniversityofNorthFlorida,Jacksonville,FLINTRODUCTIONThispaperpresentsanewassetallocationandacomparabl
2、eframeworkforanewoptimizationtechniquethroughtheuseofcointegrationanalysis.Asignificantdifferencebetweentraditionalandnewportfoliooptimizationisbaseduponthecointegrationofpricesratherthanthecorrelationofreturns.Longterminvestors,universityendowmentsormutualfundswhosereturnsareusuallycomparedagain
3、stabenchmarkhavelongterminvestmenthorizons.Commonly,theirportfoliosareoptimizedusingmean-varianceanalysis.Modelsthatarebasedonmean-varianceanalysisseekportfolioweightstominimizethevarianceoftheportfolioforagivenlevelofreturn(ex.Benchmark).Theportfoliovarianceismeasuredusingacovariancematrixandthe
4、sematricesaredifficulttoestimateandmoreimportantlydonotrepresentastablerelationship.Additionally,mean-variancecriterionhadnothingtoensurethattrackingerrorsarestationary.Replicationofaportfolioinmean-varianceframeworkcandriftveryfarfromthebenchmarkunlessit’sfrequentlyre-balanced.Themainadvantageof
5、cointegrationanalysis,ascomparedtotheclassicalbutratherlimitedconceptofcorrelation,isthatitenablestheuseoftheentireinformationsetcomprisedinfinancialvariables.Moreover,acointegrationrelationshipisabletoexplainthelongrunbehaviorofcointegratedseries,whilecorrelation,asameasureofcodepency,usuallylac
6、ksstability,beingonlyashortrunmeasure.Whiletheamountofhistorythatmaybeusedtosupportthecointegrationrelationshipmaybelarge,theattempttousethesamesampletoestimatecorrelationmayfacemanyobstaclessuchasoutliersinthedatasampleandvolatilityclustering.Theenhancedstabilityofcointegrationrelationshipgenera
7、tesanumberofsignificantadvantagesforaportfolio,forinstance,reducingtheamountofrebalancingtradeinatrackingstrategyandconsequentlytheassociatedtransactioncosts.Thispapershowsthatit’spossibletodeviseallocationsthathavesta
