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1、Nonparametricpredictionintimeseriesanalysis:someempiricalresultsMarcellaNiglioandCiraPernaAbstract.InthispaperanewapproachtoselectthelagpfortimeseriesgeneratedfromMarkovprocessesisproposed.Itisfacedinthenonparametricdomainanditisbasedontheminimisationoftheestimatedris
2、kofpredictionofone-step-aheadkernelpredictors.TheproposedprocedurehasbeenevaluatedthroughaMonteCarlostudyandinempiricalcontexttoforecasttheweakly90-dayUST-billsecondarymarketrates.Keywords:kernelpredictor,estimatedriskofprediction,subsampling1IntroductionOneoftheaimsi
3、ntimeseriesanalysisisforecastingfuturevaluestakingadvantageofcurrentandpastknowledgeofthedata-generatingprocesses.Thesestructuresareoftensummarisedwithparametricmodelsthat,basedonspecificassumptions,definetherelationshipsamongvariables.Inthisparametriccontextalargenumbe
4、rofmodelshavebeenproposed(amongothers,[3],[20],[4],and,morerecently,[11],whichdiscussesparametricandnonparametricmethods)andformostofthemtheforecastperformancehasbeenevaluated.Toovercometheproblemofpriorknowledgeaboutthefunctionalformofthemodel,anumberofnonparametricm
5、ethodshavebeenproposedandwidelyusedinstatisticalapplications.Inthiscontext,ourattentionisfocusedonnonparametricanalysisbasedonkernelmethodswhichhavereceivedincreasingattentionduetotheirflexibilityinmodellingcomplexstructures.Inparticular,givenaMarkovprocessoforderp,int
6、hispaperanewapproachtoselectthelagpisproposed.Itisbasedontheminimisationoftheriskofprediction,proposedin[13],estimatedforkernelpredictorsbyusingthesubsampling.Afterpresentingsomeresultsonthekernelpredictors,wediscuss,inSection2,howtheycanbeintroducedintheproposedproce
7、dure.InSection3wefurtherdescribethealgorithmwhoseperformancehasbeendis-cussedinaMonteCarlostudy.Toevaluatetheforecastaccuracyofthenonparametricpredictorinthecontextofrealdata,inSection4wepresentsomeresultsontheweeklyM.Corazzaetal.(eds.),MathematicalandStatisticalMetho
8、dsforActuarialSciencesandFinance©Springer-VerlagItalia2010236MarcellaNiglioandCiraPerna90-dayUST-billsecondarymarketrates.So
