TESTS OF GAUSSIAN TEMPORAL FACTOR LOADINGS IN FINANCIAL APT

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1、TESTSOFGAUSSIANTEMPORALFACTORLOADINGSINFINANCIALAPTKaiChunChiuandLeiXuDepartmentofComputerScienceandEngineering,TheChineseUniversityofHongKong,HongKong,P.R.ofChinaE-mail:kcchiu@cse.cuhk.edu.hk(CorrespondingAuthor)lxu@cse.cuhk.edu.hkFaxNo.:+(852)2603-530

2、2ABSTRACTcovariancematrixincreasewithoutboundasthenumberofThemaindifficultyoffinancialAPTanalysisconcernsiden-securitiesinthepopulationincreases,thentheelementsoftifyingunambiguouslythehiddenstatisticalfactors.Lackthecorrespondingkeigenvectorsofthecovaria

3、ncematrixofeffectivetechniquestoretrievethetruefactorsoftenleadscanbeusedasfactorsensitivities.ConnorandKorajczyktoinappropriateinterpretationoftheunderlyingfactorstruc-[7]furthershowthatthisconclusionholdsforthesampleture.Inliterature,PCAandMLFA,assumi

4、ngmultivariatecovariancematrixaswell.Gaussiandistributions,andICA,assumingnon-GaussianHowever,ShuklaandTrzcinka[8]criticizethisapproachdistributions,areusedtoextractfactorsanddeterminetheonthegroundthateigenvectorsbeingusedinsteadofsta-correspondingfact

5、orloadings.Recently,anewtechniquetisticalfactorloadingsinthereturns-generatingmodelforcalledTFAisproposedin[1,2]whichseekstosolvethealargeeconomy,possiblywithinfinitelymanyassets,doesproblemofrotationindeterminacyencounteredinconven-notnecessarilyimplyth

6、attheycanbeusedinacross-sectionaltionalfactoranalysis.Inthispaperwewillfocusonstatisti-modelofsecuritypricinginfiniteeconomies.ThereasoniscaltestsandinferenceontheAPTtemporalfactorloadingsthatPCAisverydifferentfromfactoranalysisincontextofrecoveredbyTFA.

7、finitenumberofsecurities.PCAismoreconstrainedthanfactoranalysisfortheapplicationinAPTbecauseittends1.INTRODUCTIONtooverlookidiosyncraticrisks.Inrecentyears,BackandWeigend[9]applyIndepen-ArbitragePricingTheory(APT)asanassetpricingmodeldentComponentAnalysi

8、s(ICA)tomultivariatefinancialhasattractedconsiderableinterestfrompractitionersinthetimeseriestorecoverstatisticallyindependentnon-GaussianfinancefieldsinceitwasproposedbyRoss[3]in1976.distributedfactors.Itiswell-knownthatICAwillexpl