金融科技时代是否让我们感到幸福

SocialIndicatorsResearch/10.1007/s11205-025-03579-w

ORIGINALRESEARCH

IstheFintechEraMakingUsHappy?

VanLe1 ·BaoKhacQuocNguyen1

Accepted:10March2025

©TheAuthor(s),underexclusivelicencetoSpringerNatureB.V.2025

Abstract

Thisstudyinvestigatestheinteractionsbetweenfinancialtechnology(Fintech)andhap-pinesssentimentundereconomicstabilityfrom15July2015to31December2019andglobalvolatilityfrom01January2020to31December2021.WeexaminetherelationbetweendailychangesintheFintechandhappinessproxiesbasedonthebivariategen-eralizedautoregressiveconditionalheteroskedasticitymodelingwithconditionalcorrela-tionmechanisms.WefindastatisticallysignificantresultthattheFintechproxynegativelyaffectshappinesssentiment,regardlessofeconomiccontexts.Thisimpliesatrade-offbetweenFintechandhappiness,whichisadditionallysupportedbythenegativeimpactofhappinessonFintechinthecontextofglobalvolatility.Wefurtherindicatethatthehappi-nessobjectiveshallbemoreconcentratedduringuncertainties.EmpiricalfindingsclarifyhowtheFintechinnovationischallengedandgeneratethehappinessstandardtoassesstheprospectoffinancialtechnologyinfuture.Werecommendthatpolicymakersshouldconsiderhappinessgoalsinpursuitoftechnologicalinnovationinthecontextofdigitalera.

KeywordsFintech·Happiness·Globalvolatility·BivariateGARCH

JELClassificationG41·I31·D81

Abbreviations

AIC AkaikeinformationcriterionDCC Dynamicconditionalcorrelation

DHS Twitter’sdailyhappinesssentimentindexFintech Financialtechnology

GARCH GeneralizedautoregressiveconditionalheteroskedasticityIFINX INDXXglobalFintechthematicindex

SBC SchwarzBayesianinformationcriterionVAR Vectorautoregression

VARMA Vectorautoregressivemovingaverage

VanLe

Levan@.vn

BaoKhacQuocNguyenNguyenbao@.vn

1 UniversityofEconomicsHoChiMinhCity(UEH),No.59CNguyenDinhChieuStreet,District3,HoChiMinhCity,Vietnam

IstheFintechEraMakingUsHappy?

Fintecharedemonstratedtoconnectwithinnovationfailure,noticeablyintermsofethics(Trieu,2016)andlegalissues(Buckleyetal.,

2019

).TypicalillustrationsofinnovationfailurerelatingtoFintecharetheappearanceofelectronicmoneylaundering(Facciaetal.,

2020

)anddataprivacythroughmobilemoney(Mogaji&Nguyen,

2022

).Asaresult,prosandconsofFintechcoexist,regardlessoftimeframecontexts(Bootetal.,

2021

).

Inthelongrun,Fintechrisksassociatedwithinnovationfailurenegativelyaffectsfinan-cialstability(Fungetal.,

2020

).Hence,thedarksidesofFintechcomefrombothfinancialandtechnologicalaspectsandinfluencesmultidisciplinaryissuesoflife.RelevantliteraturemotivatesustoidentifyandmeasuresuchdarksidesofFintech(Murindeetal.,

2022

),whichrelatetoinnovationfailureassociatedwithrisksofthetechnologicalrevolution.ThismotivationbecomesstrongerinthecontextofglobalvolatilityfollowingtheCOVID-19crisis(Barrafremetal.,

2021

),especiallyinpursuitofsustainability.ItisnoticeablethatthemarketvalueofFintechcompaniesisnegativelyrelatedtotheirgreencertificates(Merelloetal.,

2022

).

Motivatedbyabovediscussedissues,theobjectivestudyistodeterminetheimpactofFintechonoverallhappinessandinvestigatingwhethertheeraofFintechispositivelyinfluencingourwell-being.Inspecific,weusetheINDXXGlobalFintechThematic(IFINX

1

)andtheTwitter’sDailyHappinessSentiment(DHS

2

)indexesasmainproxiesofthisstudy.Asthetwoproxiesarecharacterizedbythetimeseriesbasis,weemploythebivariategeneralizedautoregressiveconditionalheteroskedasticity(GARCH)toinvestigatetheinteractionsbetweenFintechandhappiness.Basedonthisdesign,weproposetoiden-tifywhetherthesocialaspectofFintechisanopportunityoratrauma.Thenoveltyofthisstudypresentsinourapproachexaminingtherelationshipbetweenfinancialtechnologyandhappinesssentiment.

Thedesignofthisstudyfacilitatestodirectlyevaluatetheconnectionbetweenfinan-cialtechnologyandhappinesssentiment.Accompanyingthisresearchobjective,weusehighfrequencydatasetswithdailyobservationsofFintechandhappinesssentimentprox-ies.Thisapproachisbasedontechnologicalevaluation(Damodaran,

2001

).Besides,weemployasophisticatedmethod(asdetailedinSub-Sect.

3.2

)toinvestigatepotentialinter-actionsbetweenFintechandhappinesssentimentintermsofreturnandvolatilitytrans-missions.Beyondthesematerialsandmethods,thenoveltyofthisstudycomprisesoftheglobalvolatilitycontext,asremarkedbytheunprecedentedCOVID-19crisis.Furthermore,thisstudyisexpectedtoshowthatIFINXandDHSarepromisingindicatorstoevaluatesocialissues.

Theremainderofthisstudyisstructuredasfollows.Section

2

reviewsrelevantlitera-ture.Section

3

presentsrelevantmaterialsandmethods.Section

4

presentsempiricalfind-ings.Section

5

discussesonourfindings.Section

6

concludes.

LiteratureReview

Asfinancialtechnologydemonstratestheinnovationprocess,itisprobablethatFin-techdriveseconomicgrowthpositively.Previousstudiesfoundthiscorrespondingprop-ertyofinnovationundervariousperspectives,forexample,entrepreneurship(Boekema

1Source:INDXX,retrievedfrom

/indxx-global-fintech-thematic-index-tr

.

2Source:Hedonometer,retrievedfrom

/

.

V.Le,B.K.Q.Nguyen

etal.,

2000

),researchanddevelopment(Ulku,2004),andprofessionaleducation(Bil-bao‐Osorio&Rodríguez‐Pose,

2004

).Thecombinationoffinanceandtechnologyillus-tratesthecollaborationbetweentheoryandpracticeintermsofproductivityenhance-ment(Verspagen,2005).Accordingly,innovationandFintechfacilitatethelong-termeconomicdevelopment(Hall&Rosenberg,

2010

).Inaddition,previousstudiesindi-catethattheimpactofinnovationandFintechoneconomiesshallcomealongsupport-ivefactorssuchasmanagerialknowledge(Wongetal.,2005),comprehensivepolicies(Audretsch,

2006

),andinstitutions(Tebaldi&Elmslie,2008).Theinterventionofinno-vationandFintechcanbefoundinpatents,trademarks,orresearchanddevelopment.Basedonthetheoreticalbackgroundofgrowth,thevaluableroleofinnovationandFin-techhavebeenrecentlyfoundacrossmultipleregions,forinstance,theglobe(Gyeduetal.,

2021

),developedeconomies(Doganetal.,

2022

),Europe(Peceetal.,

2015

),andtheGulf(Sarwar,2022).

Amongthedigitalera,theCOVID-19pandemicappearedtobeaninfamousplayeracrossvarioussectorsduringpreviousyears.Thisunprecedentedeventhadbeencausingenormousdamagestoglobaleconomicandsocialcontexts.Inwhich,adirectlynegativeimpactcomesfromthedisruptionofinternationalandlocalsupplychains(Barua,

2020

).Inresponse,acoldlightfortheworldcamefromdigitaleconomics,asaninevitableconse-quenceofthefourthindustrialrevolution.Thetremendousadvantageofdigitaleconomicsisloweringcostswhichareassociatedwithrelevantsupplychains(Goldfarb&Tucker,

2019

).Inspecific,lowercostsassociatedwithdigitaleconomicscomefromsearch,rep-lication,transportation,tracking,andverification.Thischaracteristicofthedigitaleramatchestheprocessofinnovation(Dutta&Lanvin

2012

),whichfacilitatestheglobalsup-plychain.Itisnoticeablethatlowercostsallowfirmstoenhancecompetitiveadvantagesandstrategies(Porter,

2008

).Asaphenomenonofdigitaleconomicsintheera4.0,FintechisexpectedtodrivefirmsandeconomiesrecoverfromtheCOVID-19crisis.

InthevolatilitycontextfollowingtheCOVID-19outbreak,innovationandfinancialtechnologyarenotexceptionsfromglobalchallenges(Wangetal.,2022),especiallyinpursuitofsustainabledevelopmentgoals.Inthecurrentworldofvolatility,uncertainty,complexity,andambiguity,recentstudiesstatethatinnovationandFintechshallcon-nectwithkeyfactorssuchasregionaleconomy(Xuetal.,2022)andbusinessstrategies(Valdez-Juárezetal.,2022).Inpursuitofsustainabledevelopmentgoals,theworldisfac-inggrandchallenges.Accordingtothetheoryofdigitaltechnologyadvancement(Pop-kovaetal.,

2022

),theinnovationofFintechfacilitatesthesolutionoftheproblem.Specificapplicationsrelatetosustainabledevelopmentgoalnumber3(goodhealthandwell-being),number16(peace,justice,andstronginstitutions),andnumber17(partnershipsforthegoals),respectively.InthedigitaleraofIndustry4.0,technologiesarefoundtobeakeyfactorofcircularsupplychains(Gebhardtetal.,

2022

).Moreover,thelinkagebetweendig-italizationtechnologiesandcirculareconomyisaffirmedviapastachievements(Chauhanetal.,

2022

),andthereforeshedslightonfuturescenarios.Hence,previousliteraturehasprovidedareliablebackgroundaccompanyingempiricalevidenceregardingthepositiveimpactofinnovationandFintechoneconomicgrowth.

EventhoughFintechcreatescomfortableaccesstofinanceforfirms,investors,andreg-ulators(Bollaertetal.,

2021

),itcontainsdarksidesoffinancialinnovation(Henderson&Pearson,

2011

).Inessence,financialtechnologyischallengedinconnectinghumanandcomputer.Thisthreatisremarkablewhenmathematicalalgorithmsaredesignedtocap-turepsychologicalandcognitivefunctions(Das,

2019

).DifficultiesforFintechisfoundinfinancialinstitutions(Lietal.,

2020

),whichincludebanks,securitiesfirms,andinsurancefirms.ThisconsequenceisfoundintheUnitedStates,thecradleoftechnologicalfirmsand

IstheFintechEraMakingUsHappy?

mayspreadtoothercountriesbasedonriskspilloversintheflatworld.PreviousstudiesclaimedthatthebankingsectorishighlysensitivetoFintechinnovation,fromlenders(Jag-tiani&Lemieux,

2018

)torisk-takingbehavior(Bannaetal.,

2021

),andcreditprovision(Sheng,2021).EventhoughFintecharisestomitigateintermediarycostsassociatedwithfinancialservices,itseemstogeneratesideeffectsonthesectoracrosseconomies.

Moreover,Fintechisfoundtointeractwithsmallandmediumenterprisesaswellasnationalcultures(Abbasietal.,

2021

).Insummary,challengesforFintechcomefromvari-ousaspectsofbothfinanceandtechnology,whichisdescribedasdivergenteffects(Fungetal.,

2020

).Inwhich,marketcharacteristicssuchasfragilityandstateofdevelopmentcontributetoexplainimpactsofFintechinnovation.Thisrelevantliteratureshowsthattheevolutionaryprocessoffinancialtechnologycontainsvariousaspects.Henceforth,itremainsacontroversytoconfirmwhetherFintechpositivelyornegativelydrivesourqual-ityoflife.Toclarifythispuzzle,weaimtoinvestigatehowFintechinfluenceshappinesssentiment.Thissolutionisexpectedtorevealpotentialfindingswhilsthappinessshallbethefavorableobjectiveofhumanbeingsascomparedtofinancialwealth(Nguyen&Le,

2021

).

Fromourindicatorselection,IFINXrepresentstheperformanceoflistedfirmsindevel-opedmarketswhichareofferingfinancialproductsdrivenbytechnology.Thisproxycap-turesfinancialservicesandbankingsector,illustratingtheFintechdevelopmentanditseconomicbenefit.Besides,DHSrevealshappinessaroundtheworldbasedonthewordi-nessalgorithm(Kramer,

2010

),appliedforpostsviasocialnetworks.Thankstoitsdailytimeseriescharacteristic,DHSisempiricallyfoundtobeeffectiveinexploringtherela-tionshipbetweenhappinessandfinancialmarkets,forexample,theUnitedStates(Zhao,2020),globalstock(Nguyen&Le,

2021

),alternativeinvestments(Lê&Nguyễn,

2022

).,andsolarenergy(Le,

2024

).Thus,ourusageofIFINXandDHSmatchespriorliteratureinexaminingtherelationshipbetweenfinancialassetsandabehavioralfactorsuchashappi-nesssentiment.ThisproxyselectionisexpectedtoprovideempiricalfindingsonwhethertheFintecheraismakingushappyundervariousperspectives.

Asabovepresented,previousliteraturehasindicatedimpactsoffinancialtechnologyonthelife.Thoseimplicationsvaryacrosseconomicandsocialaspectswithbothpositiveandnegativecharacteristics.Whentechnologyandinnovationcontributetothepursuitofhappiness,theimpactofFintechonwell-beingiscomplicatedundermultipleperspectives.InrelationtothissocialissueregardingtheconnectionbetweenFintechandhappinesssen-timent,wedesignthisstudytocaptureanyfeasibleinteractionbetweenthetwoindica-tors,thoseare,IFINXandDHS.ThankstothebivariateGARCHmodeling(asdetailedinSub-Sect.

3.2

),thepotentialresultshallbe(i)IFINXsignificantlydrivesDHS;(ii)DHSsignificantlydrivesIFINX;(iii)both(i)and(ii);or(iv)neither(i)nor(ii).Withineachinteraction,theimpactcouldbepositiveornegative.Therefore,thisproposedmethodol-ogyisexpectedtorevealafinaljudgmentforcontrastingconclusionfrompreviousstudiesregardingimpactsofFintech,frompositivity(Merelloetal.,

2022

;Murindeetal.,

2022

;Pizzietal.,

2021

)tonegativity(Bollaertetal.,

2021

;Fungetal.,

2020

;Henderson&Pear-son,

2011

).

V.Le,B.K.Q.Nguyen

Fig.1Datasetsillustration.Sources:INDXX(IFINX)andHedonometer(DHS)

DataandMethodology

Data

MaterialsofthisstudyincludetheINDXXGlobalFintechThematicindex(IFNIX)andtheTwitter’sDailyHappinessSentimentindex(DHS).Theresearchperiodisfrom15July2015to31December2021.Inwhich,thisperiodcaptureseconomicstability(until31December2019)andtheglobalvolatilitycontextasremarkedbythecommencementoftheunprecedentedCOVID-19event(from01January2020).Figure

1

visuallyillustrates

Table1Summarystatisticsofreturnseries

15Jul2015–31Dec

2019

01Jan2020–31Dec

2021

IFINX

DHS

IFINX

DHS

Numberofobservations

1159

1159

497

497

Mean

0.07%

0.00%

0.07%

0.00%

Standarddeviation

1.11%

0.51%

1.98%

0.62%

Skewness

−0.9798***

−0.7376***

0.5019***

−1.2918***

Kurtosis

3.9967***

10.2444***

22.0669***

10.9787***

Jarque–Bera

956.83***

5173.18***

10,105***

2634.22***

Ljung-Box

32.7996***

183.50***

29.7307**

50.3042***

McLeod-Li

260.07***

97.0889***

250.33***

139.15***

ARCH

117.81***

72.1400***

142.03***

106.22***

Dickey-Fuller

−30.1916***

−41.4090***

−22.2189***

−23.5881***

Phillips-Perron

−30.1281***

−49.9851***

−22.2923***

−24.4243***

*,**,and***indicatestatisticalsignificanceof10%,5%,and1%,respectively.Descriptivestatisticsincludenumberofobservations,mean,standarddeviation,skewness,andkurtosis.StochasticpropertiesincludenormalitywiththeJarque–Beratest,theautocorrelationeffectwith15-laggedLjung-BoxandMcLeod-Litests,heteroskedasticitywitha15-laggedARCHtest,andstationaryconditionswithaugmentedDickey-FullerandPhillips-Perronunitroottests.Software:ESTIMARATS

IstheFintechEraMakingUsHappy?

thedatasets,highlightingtheuncertaintiesaroundtheCOVID-19outbreakin2020.ThispatternpreliminaryaffirmsoursplittingthedatasetsintoseparateperiodstoexaminetherelationshipbetweenFintechandhappinessunderimpactsoftheCOVID-19pandemic.

Table

1

presentssummarystatisticsofIFINXandDHSreturnseriesduringconsideredsub-periods.Wecomputedailychangesintermsofnaturallogarithmtoobtainthecontinu-ousreturnseriesofIFINXandDHS.Briefly,bothFintechandhappinessreturnstendtoremainintermsofmeanbutvolatilemoreintermsofstandarddeviationunderimpactsoftheCOVID-19pandemic.TheskewnessoftheIFNIXreturnseriesinversesfromnegativ-itytopositivityduringseparatesub-periods.WhilsttheDHSskewnessremainsnegativeunderimplicationsoftheoutbreak.Additionaldescriptivestatisticsaffirmthatbothreturnseriesareleptokurticandnormallydistributed.Thestochasticityofbothreturnseriesaresupportedwithstatisticallysignificantevidenceregardingautocorrelationandheter-oskedasticityeffects.Ontheotherhand,thestationaryconditionofbothreturnseriesareensuredduringbothsub-periods.Thesestochasticpropertiesmotivateustouseasophisti-catedlytimeseriesframework,thoseare,bivariateGARCHmodelstoexaminetherelation-shipbetweenrepresentativesoffinancialtechnologyandhappinesssentiment.

Methodology

Asreturnseriesaresignificantlyfoundtobestochasticwithtimeseriesproperties,weusethebivariategeneralizedautoregressiveconditionalheteroskedasticity(GARCH)frame-worktoexploretherelationshipbetweenFintechandhappinessreturnseries.Thismod-elingstrategyisempiricallydemonstratedtobeeffectiveinexplainingthenexusbetweenmarketindexesandhappinesssentiment,forexample,globalwealth(Nguyen&Le,

2021

)andfinancialassets(Lê&Nguyễn,

2022

).Theinitialstepinthisprocessistoestimatetheinteractionsbetweenreturnseriesintermsofavectorautoregressive(VAR)meanequa-tion.ThankstoAkaike(AIC)andSchwarzBayesian(SBC)informationcriteria,weinves-tigatetherelationshipbetweenIFINXandDHSreturnseriesinasix-laggedVARmodelduringbothsub-periodsasfollow:

� ∑r=µ+ Φr +t:

6

t j=1jt−j t

t

t:t=H0.5t

(1)

In(

1

),r

=rxry

�isthedailychangeinIFINXandDHSattimet.Thevectorof

t tt

interceptsisdenotedbyµ=µxµy�.Thejthlagged(2×2)matrixofcoefficientsis

denotedbyΦ.Thevectoroferrortermsisdenotedbye=exey�.Thetime-varying

j t t t

covariancematrixofreturnseriesisdenotedbyHt,whoseCholeskyfactorisH0.5.The

t

xy xy

vectorofconditionalvarianceisVt=htht�andthecovariancetermisht.Thevector

ofstandardizedresidualsisn=nxny�,whoseelementsareidenticallyandindepen-

dentlydistributed.

t t t

ThesecondstepofabivariateGARCHframeworkistoexaminethevolatilitytrans-missionbetweenreturnseries.Consideringthetrade-offbetweenfeasibilityandflex-ibilityofsuchprocess(DeAlmeidaetal.,

2018

),weestimatetheconditionalcovariancematrixbasedonconstantanddynamicconditionalcorrelationmechanisms.Technically,AICandSBCinformationcriteriarevealthatthesix-laggedassumptionisoptimalfor

bothshort-andlong-termvolatilitiesmodeling,whicharerespectivelyindicatedbyetandtheithlaggedHt−i.Inotherwords,weimposep=q=6forthebivariateGARCH(p,q)

V.Le,B.K.Q.Nguyen

process.Ontheotherhand,theusageofvariousmodelsensurestherobustnesscheckofourestimations.

Giventheconstantconditionalcorrelation,weemploytheVARMA-GARCHmodel(Ling&McAleer,

2003

)anditsspecialversion,theCCC-GARCHmodel(Boller-slev,

1990

)toexaminethevolatilitytransmissionbetweenreturnseries.Inspecific,theVARMA-GARCHmodelcapturesthevolatilityspilloverbetweenreturnsbasedonavec-torautoregressivemovingaverageassumption.WhilsttheCCC-GARCHmodelsimplifiestheprocessbyimposingthatestimatedmatricesarediagonal.Theschoolofconstantcon-ditionalcorrelationassumesthatreturnseriescorrelateconstantlytoeachother(throughp)andestimatesHtasfollow:

� ∑ ∑

q 2 p

t

t

t

Vt=C+ i=1Ait:t−i+ i=1BiVt−ihxy=p�hxhy

(2)

In(

2

),C=cxcy�isavectorofintercepts.Theithlaggedshort-termvolatilitytrans-missionisreflectedbymatricesAi.Theithlaggedlong-termvolatilitytransmissionisreflectedbymatricesBi.Theassumedconstantconditionalcorrelationtermbetweenreturnseriesisdenotedbyp.Specifically,theVARMA-GARCHmodelgeneralizes(

2

)byassum-ingthatvolatilitytransmissionmatricesarefullysized.Thisapproachfacilitatestocapturethereturnandvolatilityspilloverseffect.Besides,theCCC-GARCHmodelsimplifies(

2

)byimposingthatvolatilitytransmissionmatricesarediagonal,makingitselfaspecialver-sionoftheVARMA-GARCHmodel.Regardingthestationarycondition,wearesupposedtoensurethateigenvaluesof(Ai+Bi)matricesareinsidetheunitcircle.

Giventhedynamicconditionalcorrelation,weemploytheDCC-GARCHmodel(Engle,

2002

)toexaminethevolatilitytransmissionbetweenreturnseries.Asthismodelmaygenerateinadequateestimations(Alietal.,

2022

),wefurtherconsiderthisprocessunder

bothGaussiannormalandStudent’stdistributions.In(

2

),pbecomesptandHtisfurtherdecomposedas:

Ht=DtPtDt

(3)

In(

3

),Ptisthetime-varyingconditionalcorrelationmatrixbetweenreturnseriesandDt

isthediagonalmatrixincludingthetime-varyingstandarddeviationsofeachreturnseries.

Inotherwords,D=diag�√hx,�hy�.TheDCC-GARCHmodelfurtherestimatespas

t t t t

follow:

( ) ( )=Q QQ

t

t

t

t

(4)

fP ∗−0.5 ∗−0.5

Qt=(1−a−{J)Q+ar,t−1ht−1�+{JQt−1

t

In(

4

),Qtisapositivelydefinitematrix(Qt≻0),Q∗=diagQt,Q=Entht�,andDCC-GARCHcharacterizedparametersareand/J,imposedtobenon-negativesca-larssuchthat𝛼+𝛽<1.Weadditionallyestimatethedegreeoffreedom()whencon-sideringtheStudent’stdistributionofresidualseriesasretrievedfromtheDCC-GARCHestimation.

IstheFintechEraMakingUsHappy?

Table2EstimationsunderconstantconditionalcorrelationGARCHmodels

Models

VARMA-GARCH

CCC-GARCH

Period

2015–2019

2020–2021

2015–2019

2020–2021

rx

ry

rx

ry

rx

ry

rx

ry

t

µ 0.0013***

0.0000

0.0010

0.0002

0.0013***

−0.0002

0.0015***

0.0001

rx 0.0748***

−0.0038

0.1728***

0.0066

0.0917***

−0.0001

0.1959***

−0.0148

ry −0.0243

−0.4130***

0.2520**

−0.1502***

0.0398

−0.4114***

−0.0143

−0.1750***

rx −0.0044

−0.0220**

0.0481*

0.0147**

−0.0387

−0.0240**

0.1014***

0.0019

ry −0.0600**

−0.4299***

−0.1735

−0.2648***

−0.0563

−0.4208***

−0.2796***

−0.2481***

rx −0.0397

−0.0086

−0.0193

0.0126

−0.0004

−0.0106

−0.0001

0.0145

ry 0.0246

−0.3850***

0.1102

−0.1269***

−0.0133

−0.3570***

−0.0082

−0.1567***

rx −0.0398

−0.0101

0.0120

−0.0029

−0.0557**

−0.0059

0.0003

−0.0003

ry −0.0631

−0.2615***

−0.1038

−0.0841**

−0.0318

−0.2752***

−0.1726**

−0.1192***

rx −0.0691***

−0.0018

−0.0196

−0.0077

−0.0567**

0.0023

−0.0105

−0.0125

ry 0.0590

−0.0869***

0.0495

0.0348

0.0482

−0.0866***

0.0950***

0.0380

rx 0.0005

−0.0054

−0.0046

−0.0309***

−0.0145

−0.0151

−0.0247***

−0.0281**

ry −0.0142

−0.0912***

−0.0539

−0.1084***

0.0231

−0.0282*

−0.1086

−0.0713

p 0.0258

Information

0.0906**

0.0327

0.0953**

N 1153

491

1153

491

LogL 8331.89

AIC −14.3190

SBC −13.9820

HQ −14.1920

Tests

3265.92

−12.9890

−12.3310

−12.7310

8364.79

−14.4180

−14.1860

−14.3300

3305.09

−13.2470

−12.7940

−13.0690

JB 192.61***

7214.53***

31.7380***

427.19***

204.35***

8131.81***

25.9500***

173.10***

LB 7.5714

44.6235***

23.0683*

33.5123***

5.5708

47.8743***

19.8523

24.5972*

ML 12.5650

9.4409

65.9611***

73.8022***

6.2688

4.3234

5.6200

7.5714

t t t t t t t

t−1t−1t−2t−2t−3t−3t−4t−4t−5t−5t−6t−6

V.Le,B.K.Q.Nguyen

Table2(continued)

Models VARMA-GARCH CCC-GARCH

Period 2015–2019 2020–2021 2015–2019 2020–2021

rx

ry

rx

ry

rx

ry

rx

ry

ARCH

12.6240

8.9290

39.1920***

61.3270***

6.3890

4.1280

5.4040

7.0890

Portfolio

rT

0.1528

0.8472

0.0875

0.9125

0.1660

0.8340

0.1011

0.8989

/Jyx

0.0930

0.3766

0.0911

0.3346

T

RT

0.01%

0.01%

0.01%

0.01%

.T

0.47%

0.61%

0.46%

0.61%

RT

2.44%

1.33%

2.64%

1.47%

0.8240

0.9057

0.8244

0.9047

t t t t t t t t

*,**,and***indicatestatisticalsignificanceof10%,5%,and1%,respectively.NumberofobservationsisdenotedbyN.LoglikelihoodisdenotedbyLogL.InformationcriteriaincludeAkaike(AIC),SchwarzBayesian(SBC),andHannan-Quinn(HQ)terms.TheJarque–Bera(JB)testisfornormalityofresiduals.Theautocorrelationeffectischeckedwith15-laggedLjung-Box(LB)andMcLeod-Li(ML)tests.TheARCH(15lags)testisaheteroskedasticitycheck.PortfolioassessmentsincludeFintechweight(rx)

T

andhappinessweight(ry)inwhichr=rxry,hedgeratio(/Jyx),return(R),standarddeviation(.),risk-adjustedreturn(R),andhedgingeffectivenessratio().Esti-

T T TT T T T T

mationwiththeBroyden-Fletcher-Goldfarb-Shanno(BFGS)algorithm.Software:ESTIMARATS

IstheFintechEraMakingUsHappy?

Table3EstimationsunderdynamicconditionalcorrelationGARCHmodels

0.0014***

−0.0001***

0.0013***

0.0000

0.0017***

0.0001***

0.0020***

0.0002

0.1057***

−0.0047

0.1369***

−0.0140*

0.0606**

−0.0019

0.0909***

0.0017

0.1103**

−0.4133***

−0.0130

−0.1846***

0.0070

−0.4309***

0.0522

−0.1828***

−0.0504**

−0.0197*

0.0871***

0.0048

−0.0367

−0.0063

0.0794**

0.0076

−0.0252

−0.3866***

−0.2794***

−0.2471***

−0.0625

−0.4393***

−0.1684*

−0.2846***

−0.0004

−0.0095

−0.0112

0.0128

0.0062

−0.0103

−0.0218***

0.0019

0.0209

−0.4059***

−0.0678

−0.1536***

0.0444

−0.3570***

−0.0286

−0.1410***

−0.0814***

−0.0028

−0.0084

0.0021

−0.0679***

−0.0130***

−0.0479***

0.0080

−0.0031

−0.2631***

−0.1859**

−0.1144***

−0.0582

−0.2774***

−0.1239

−0.0945**

−0.0394*

0.0009

0.0322*

−0.0138

−0.0373

−0.0104***

0.0542

−0.0127

0.0350

−0.0530***

0.0546

0.0533

0.0054

−0.0040*

0.1288

0.0633*

−0.0281

−0.0156

−0.0413***

−0.0283**

−0.0171

0.0065

−0.0050

−0.0201*

Models

DCC-GARCH

DCC-GARCH

(Student’st)

Period

2015–2019

2020–2021

2015–2019

2020–2021

rx ry

rx

ry

rx

ry

rx

ry

t

t t t t t t t

µ

r

r

xt−1y

r

r

t−1xt−2y

r

r

t−2xt−3y

r

r

t−3xt−4y

r

r

t−4xt−5y

r

t−5xt−6

t−6

−0.0162***

0.0469***

0.0045

0.0409

/J

Information

0.5072***

0.2753***

0.4047***

4.1708***

0.6716***

5.8043***

N

1153

491

1153

491

LogL

8345.73

3309.31

8552.27

3334.47

AIC

−14.3830

−13.2600

−14.7390

−13.3580

SBC

−14.1460

−12.7980

−14.4990

−12.8880

HQ

−14.2940

−13.0790

−14.6480

−13.1740

ry 0.0242 −0.0463* −0.0866 −0.0592 −0.0189 −0.0228* −0.0943 −0.0435

V.Le,B.K.Q.Nguyen

Table3(continued)

Models

DCC-GARCH

DCC-GARCH

(Student’st)

Period

2015–2019

2020–2021

2015–2019

2020–2021

rx ry

rx

ry

rx

ry

rx

ry

t

Tests

JB

177.