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1、ClusteringmutualfundsbyreturnandrisklevelsFrancescoLisiandEdoardoOtrantoAbstract.Mutualfundsclassifications,oftenmadebyratingagencies,areverycommonandsometimescriticised.Inthiswork,athree-stepstatisticalprocedureformutualfundsclassificationisproposed.Inthefirststepfundtimeseriesarecha
2、racterisedintermsofreturns.Inthesecondstep,aclusteringanalysisisperformedinordertoobtainclassesofhomogeneousfundswithrespecttotherisklevels.Inparticular,theriskisdefinedstartingfromanAsymmetricThreshold-GARCHmodelaimedtodescribeminimum,normalandturmoilrisk.Thethirdstepmergestheprevi
3、oustwo.Anapplicationto75Europeanfundsbelongingto5differentcategoriesispresented.Keywords:clustering,GARCHmodels,financialrisk1IntroductionThenumberofmutualfundshasgrowndramaticallyoverrecentyears.Thishasledtoanumberofclassificationschemesthatshouldgivereliableinformationtoinvestorson
4、featuresandperformanceoffunds.Mostoftheseclassificationsareproducedbynationalorinternationalratingagencies.Forexample,Morningstargroupsfundsintocategoriesaccordingtotheiractualinvestmentstyle,portfoliocomposition,capitali-sation,growthprospects,etc.Thisinformationisthenused,together
5、withthatrelatedtoreturns,risksandcosts,tosetupamoreconciseclassificationcommonlyreferredtoasStarRating(see[11]fordetails).Actually,eachratingagencyhasaspecificownerevaluationmethodandalsonationalassociationsofmutualfundsmanagerskeepandpublishtheirownclassifications.Problemsariseas,ing
6、eneral,classesofdifferentclassificationsdonotcoincide.Also,allclassificationprocedureshavesomedrawback;forexample,theyareoftenbasedonsubjectiveinformationandrequirelongelaborationtime(see,forexample,[15]).Inthestatisticalliterature,classificationoffinancialtimeserieshasreceivedrel-ativ
7、elylittleattention.Inaddition,tothebestofourknowledge,therearenocompar-isonsbetweendifferentproposedclassificationsandthoseoftheratingagencies.Someauthorsuseonlyreturnsforgroupingfinancialtimeseries.Forexample,[15]proposeM.Corazzaetal.(eds.),MathematicalandStatisticalMethodsforActuar
8、ialSciencesandFinance©Spri