IMF-加密货币周期与美国货币政策（英）-2023.8-

TheCryptoCycleand

USMonetaryPolicy

NatashaChe,AlexanderCopestake,DavideFurceri,and

TammaroTerracciano

WP/23/163

IMFWorkingPapersdescriberesearchin

progressbytheauthor(s)andarepublishedto

elicitcommentsandtoencouragedebate.

TheviewsexpressedinIMFWorkingPapersare

thoseoftheauthor(s)anddonotnecessarily

representtheviewsoftheIMF,itsExecutiveBoard,

orIMFmanagement.

2023

AUG

©2023InternationalMonetaryFund

WP/23/163

IMFWorkingPaper

AsiaandPacificDepartment

TheCryptoCycleandUSMonetaryPolicy

PreparedbyNatashaChe,AlexanderCopestake,DavideFurceri,andTammaroTerracciano*

AuthorizedfordistributionbyJayPeiris

IMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicitcommentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseoftheauthor(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.

ABSTRACT:WeexaminefluctuationsincryptomarketsandtheirrelationshipstoglobalequitymarketsandUSmonetarypolicy.Weidentifyasinglepricecomponent—whichwelabelthe“cryptofactor”—thatexplains80%ofvariationincryptoprices,andshowthatitsincreasingcorrelationwithequitymarketscoincidedwiththeentryofinstitutionalinvestorsintocryptomarkets.Wealsodocumentthat,asforequities,USFedtighteningreducesthecryptofactorthroughtherisk-takingchannel—incontrasttoclaimsthatcryptoassetsprovideahedgeagainstmarketrisk.Finally,weshowthatastylizedheterogeneous-agentmodelwithtime-varyingaggregateriskaversioncanexplainourempiricalfindings,andhighlightspossiblespilloversfromcryptotoequitymarketsiftheparticipationofinstitutionalinvestorseverbecamelarge.

JELClassificationNumbers:E44;E52;F33.

Keywords:USMonetaryPolicy;Cryptoassets;StockMarkets.

DFurceri@;TTerracciano@

Author’sE-MailAddress:NChe@;ACopestake@;

*Firstdraft:August2022.Che(NChe@),Copestake(ACopestake@)andFurceri(DFurceri@)areattheInternationalMonetaryFund.Terracciano(TTerracciano@)isattheIESEBusinessSchool(Barcelona).WearegratefultoItaiAgur,JohnCampbell,GabrielaCondeVitureira,ShaenCorbet,HaradHau,CharlesLarkin,JayPeiris,MichaelRockinger,KennethRogoff,JeremyStein,BrandonTan,SaskiaterEllen,FabioTrojaniandseminarparticipantsattheIMFandtheUniversityofGenevafortheirconstructivecomments.Remainingmistakesareourown.TheviewsexpressedinthispaperarethoseoftheauthorsandshouldnotbeattributedtotheIMF,itsExecutiveBoard,orIMFmanagement.

WORKINGPAPERS

TheCryptoCycleand

USMonetaryPolicy

PreparedbyNatashaChe,AlexanderCopestake,DavideFurceri,and

TammaroTerracciano

2

1Introduction

Cryptoassetsvarysubstantiallyintheirdesignandvaluepropositions,yettheirpriceshavemovedincommoncycles.

1

TotalcryptomarketcapitalizationboomedfromUS$20billionin2016toalmostUS$3trillioninNovember2021,beforecollapsingtobelowUS$1trillioninthelatestcrypto‘winter’.

2

Periodsofexponentialreturnshaveattractedretailandinstitu-tionalinvestorsalike(

BenettonandCompiani

,

2022

;

AuerandTercero-Lucas

,

2021

;

Auer,

Farag,Lewrick,Orazem,andZoss

,

2022

),whilesubsequentcrasheshavedrawnincreasingattentionfrompoliticiansandregulators.Thesefluctuationsincryptomarketsmayalsobeincreasinglysynchronizedwithotherassetclasses:priorto2020,Bitcoinprovidedapartialhedgeagainstmarketrisk,yetithassincebecomeincreasinglycorrelatedwiththeS&P500(

Adrian,Iyer,andQureshi

,

2022

).

However,weknowrelativelylittleaboutthecommondriversofcryptoassetpricesorthefactorsa↵ectingthecorrelationbetweencryptoandequitymarkets,includingUSmonetarypolicy.Thispapertriestoshedlightontheseissuesbyansweringthefollowingquestions.Towhatextentisthereacommoncycleacrosscryptoassets?Arecryptomarketsbecomingmoresynchronizedwithglobalequitymarkets?Ifso,why?GiventhatUSmonetarypolicyhasbeenidentifiedasakeydriveroftheglobalfinancialcycle(

Miranda-AgrippinoandRey

,

2020

),doesUSmonetarypolicyinfluencethecryptocycletoasimilarextent?Ifso,throughwhichchannels?

Westartansweringthesequestionsbyusingadynamicfactormodeltoidentifyasingledominanttrendincrypto-assetprices.Usingapanelofdailypricesforseventokenscreatedbefore2018,whichtogetheraccountforapproximately75%oftotalcryptomarketcapital-ization,wedecomposetheirvariationintoasset-specificidiosyncraticdisturbancesandan

1Forinstance,thewhitepapersofprominentcryptoassetsincludeaimstoprovidepeer-to-peerelectroniccash,moreefficienttransactions,censorship-resistantdecentralizedcomputing,andfunctionalitywithinafinancialservicesecosystem(

Nakamoto

,

2008

;

Buterin

,

2014

;

RippleLabsInc.

,

2014

;

Binance

,

2017

;

Sun

,

2018

).Weexcludestablecoinsfromouranalysis,astheyareintendedtomaintainaconstantprice.

2Source:

CoinMarketC

.

3

AR(q)commoncomponent.Wefindthattheresulting“cryptofactor”explainsapproxi-mately80%ofthevarianceinthecryptopricedata.Thisissubstantiallylargerthanthe20%figureforglobalequitiescalculatedby

Miranda-AgrippinoandRey

(

2020

),whichalsoreflectsthegreaterconcentrationofmarketcapitalizationinthelargestcryptoassetsrel-ativetothatinthelargestequities.Thisfigureisrobustforvariouslagordersq,andwefindasimilarlyhighdegreeofcorrelationwhenbroadeningthepaneltoincludemorecryptoassets.

3

Inasecondstep,westudytherelationshipofthiscryptofactortoasetofglobalequityfactors,constructedusingtheequityindicesofthelargestcountriesbyGDP(inthespiritof

Rey

,

2013

;

Miranda-AgrippinoandRey

,

2020

).Wefindapositivecorrelationovertheentiresample,drivenbyaparticularlystrongcorrelationsince2020.Theincreasingco-movementisnotlimitedtoBitcoinvis-a-vistheS&P500,butpertainsmorebroadlytothecryptoandglobalequityfactors.Disaggregatingacrossequitymarkets,wefindthatthecryptofactorcorrelatesmoststronglywiththeglobaltechfactorandthesmall-capfactorsince2020,whileitissurprisinglylesscorrelatedwiththeglobalfinancialfactor.

Theincreasedcorrelationbetweencryptoandequitiescoincideswiththegrowthintheparticipationofinstitutionalinvestorsincryptomarketssince2020.Althoughinstitutions’exposureissmallrelativetotheirbalancesheets,theirabsolutetradingvolumeismuchlargerthanthatofretailtraders.Inparticular,thevolumeoftradingbyinstitutionalinvestorsincryptoexchangesincreasedbymorethan1700%(fromroughly$25billiontomorethan$450billion)from2020Q2to2021Q2(

Aueretal.

,

2022

).Sinceinstitutionalinvestorstradebothstocksandcryptoassets,thishasledtoaprogressiveincreaseinthecorrelationbetweentheriskprofilesofmarginalequityandcryptoinvestors,whichinturnisassociatedwithahighercorrelationbetweentheglobalequityandcryptofactors.Whendecomposingfactormovementsfollowing

Bekaert,Hoerova,andLoDuca

(

2013

),wefindthatcorrelationinthe

3Sincemostcryptoassetshavebeencreatedonlyinthelastcoupleofyears,abroaderpanelofassetsalsoimpliesashortertimedimension—hencewefocusonthesevenmainassetsinourbaselinemeasure.

4

aggregatee↵ectiveriskaversionofcryptoandequitiescanexplainalargeshare(upto65%)ofthecorrelationbetweenthetwofactors.

SinceUSmonetarypolicya↵ectstheglobalfinancialcycle(

Miranda-AgrippinoandRey

,

2020

),thehighcorrelationbetweenequitiesandcryptosuggestsasimilarimpactoncryptomarkets.WetestthishypothesisusingadailyVARwiththeshadowfederalfundsrate(SFFR)by

WuandXia

(

2016

)toaccountfortheimportantroleofbalancesheetpolicyoveroursampleperiod.OuridentificationoftheimpactofmonetarypolicyshocksisbasedonaCholeskydecompositionwiththefollowingordering:theSFFR;theTreasury10Y2Yspread,reflectingexpectationsoffuturegrowth;thedollarindex,oilandgoldprices,asproxiesforinternationaltrade,creditandcommoditycycles;theVIX,reflectingexpectedfutureuncertainty;andfinallytheequityandcryptofactors.Inthissetup,endogeneityisnotlikelytobeanissueasitisimplausiblethattheFederalReserveadjustsitsmonetarypolicyaccordingtocryptopricemovementsandthatitdoessoatthedailylevel.

4

WefindthatUSmonetarypolicya↵ectsthecryptocycle,asitdoeswiththeglobalequitycycle,contrastingstarklywithclaimsthatcryptoassetsprovideahedgeagainstmarketrisk.AonepercentagepointriseintheSFFRleadstoapersistent0.15standarddeviationdeclineinthecryptofactoroverthesubsequenttwoweeks,relativetoa0.1standarddeviationdeclineintheequityfactor.

5

Interestingly,aswiththeglobalfinancialcycle(

Rey

,

2013

),wefindthatonlytheUSFed’smonetarypolicymatters,andnotthatofothermajorcentralbanks—likelyreflectingthatcryptomarketsarehighlydollarized.

6

Wefindevidencethattherisk-takingchannelofmonetarypolicyisanimportantchannel

4Notethatourresultsarealsorobusttorelaxingtheaforementionedvariableordering.Whenweinverttheorderofthevariablestoallowthepolicyratetobethemostendogenousone,wefindsimilarresults.Asexpected,wealsofindthatthepolicyratedoesnotrespondtochangesinthecryptofactor.Thus,ourfindingsdonotdependonanarbitraryorderingofthevariables.Inaddition,usingtheavailable

Bu,Rogers,

andWu

(

2021

)monetarypolicyshocks;westillfindasignificantnegativee↵ectofUSmonetarypolicyonthecryptocycleatthemonthlylevel.

5Thisreferstostandarddeviationsofvariationincryptoorequitiesover2018-2023,theperiodforwhichwecanconstructthecryptofactor.

6Forinstance,thetwolargeststablecoinsTetherandUSDCoinarepeggedtothedollar,whileCoinbase,thelargestcentralizedcryptoexchange,islistedontheNewYorkStockExchange.

5

drivingtheseresults,parallelingthefindingsof

Miranda-AgrippinoandRey

(

2020

)forglobalequitymarkets.Inparticular,wefindthatamonetarycontractionleadstoareductionofthecryptofactorthatisaccompaniedbyasurgeinaproxyfortheaggregatee↵ectiveriskaversionincryptomarkets.Putdi↵erently,restrictivepoliciesrendertheriskpositionsofin-vestorslesssustainable,andthustheyreducetheirexposuretocryptoassets.Whensplittingthesamplein2020,wefindthattheimpactonriskaversionincryptomarketsissignificantonlyforthepost-2020period,consistentwiththeentryofinstitutionalinvestorsreinforcingthetransmissionofmonetarypolicytothecryptomarket.Moreformally,wefindthesameresultwhentestingthishypothesisusingasmoothtransitionVARfollowing

Auerbachand

Gorodnichenko

(

2012

),wherethetransitionvariableistheshareofinstitutionalinvestors.

Next,werationalizeourresultsinamodelwithtwoheterogeneousagents,namelycryptoandinstitutionalinvestors.Theformerareretailinvestorswhoonlyinvestincryptoassets,whilethelattercaninvestinbothstocksandcryptoassets.Crucially,cryptoinvestorsareriskaverse,whileinstitutionalinvestorsarerisk-neutralbutfaceavalue-at-riskconstraint.Wecanrewritetheequilibriumreturnsonthecryptoassetsasalinearcombinationoftheirvarianceandtheircovariancewithstocks’returns,scaledbytheaggregatee↵ectiveriskaversion.Thelattercanbeinterpretedastheaverageriskaversionoftheagents,weightedbytheirwealth.Thisimpliesthatthehighertherelativewealthofinstitutionalinvestors,themoresimilarthecryptoaggregatee↵ectiveriskaversionbecomestotheirriskappetiteandthemorecorrelatedarecryptoandequitymarkets.Sincethepresenceofinstitutionalinvestorsincryptomarketsdecreasestheaggregatee↵ectiveriskaversion,weinterprettheincreasingreactionofcryptopricestomonetarycontractionasreflectingthatmoreleveredinvestorsaremoresensitivetotheeconomiccycle(

Coimbra,Kim,andRey

,

2022

;

Adrian

andShin

,

2014

).Finally,wenotethatspilloversfromcryptotoequitiescanariseeveninoursimpleframework:ifinstitutions’cryptoholdingsbecomelarge,acrashincryptopricesreducesequilibriumreturnsinequities.

Overall,ourresultshighlightthatthecryptocycleisremarkablysynchronizedwithglobal

6

equitymarketsandreactssimilarlytomonetarypolicyshocks.Despitetherangeofexpla-nationsforcryptoassetvalues—e.g.,asaninflationhedgeorasaproviderofmoreefficientpayments,censorship-resistantcomputingorpropertyrights—mostvariationincryptomar-ketsishighlycorrelatedwithequitypricesandhighlyinfluencedbyFedpolicies.Thisalsosuggestsemergingcryptoventuresthatbenefitedfromhighcryptoreturnswerecon-comitantlysupportedbythelowinterest-rateenvironment.Finally,wefindthatgrowthininstitutionalparticipationhasstrengthenedtheseconclusionsandincreasedtheriskofspilloversfromcryptomarketstothebroadereconomy.

Literature:Thispapercontributestotheburgeoningliteratureoncryptoassetsbycon-nectingworkonspecificcryptopricesandthecompositionofcryptoinvestorstotheestab-lishedliteratureontheglobalfinancialcycle.

First,ourpaperbuildsonworkassessingthedriversofthepricesofspecificcryptoassets.Theprimaryaimofearlydevelopers,ledby

Nakamoto

(

2008

),wastoprovideanewformofdecentralizedelectroniccashthatpeoplecouldfreelyaccess.Severalscholarshavestudiedthematterthroughtheselenses(

Biais,Bisi`ere,Bouvard,andCasamatta

,

2018

;

Schillingand

Uhlig

,

2019

;

Brunnermeier,James,andLandau

,

2019

;

Cong,He,Li,andJiang

,

2021

;

Auer,

Monnet,andShin

,

2021

;

Pagnotta

,

2022

),yetthehighpricevolatilityandtherelativelylowscalabilityofexistingdistributedledgertechnologyhaveledresearcherstothinkofmostcryptotokensasassetsratherthancurrencies(seeforinstance

Liu,Tsyvinski,andWu

,

2022

;

MakarovandSchoar

,

2020

;

Scaillet,Treccani,andTrevisan

,

2020

).

7

Indeed,manycryptoassetslackexplicitfundamentalvalueorcash-flows(

MakarovandSchoar

,

2020

),andaresubjecttofragmentation,arbitrageopportunitiesandmarketmanipulation(

Griffinand

Shams

,

2020

;

Gandal,Hamrick,Moore,andOberman

,

2018

;

Foley,Karlsen,andPutnins

,

2019

).Inthispaper,weabstractfromcrypto-asset-specificpricingconsiderations,and

7Indeed,toaddresssuchhighvolatility,theindustrydevelopedstablecoins,suchasTetherorUSDCoin,whicharepeggedtoanothercurrency(mostcommonlytheUSdollar).

7

insteadconsiderthecommonmovementintheentireassetclass.Indoingso,webuildon

Iyer

(

2022

),whoprovidesevidenceofthepositivecorrelationbetweenUSequitymarketsandBitcoinandEtherprices,and

Corbet,Larkin,Lucey,Meegan,andYarovaya

(

2020

)whoassesstheimpactofmacroeconomicnewsonBitcoinreturns.

Second,wedrawonanemergingempiricalliteratureexaminingthecompositionandmotivationsofcryptoinvestors,includingtheincreasedparticipationofinstitutions.

Auer

andTercero-Lucas

(

2021

)studytheprofileofUScryptoinvestorsandhighlightthattheyareingenerallessmotivatedbydistrustinthetraditionalfinancialsystemthanbytheprospectsforhighreturns.

8

Auer,Farag,Lewrick,Orazem,andZoss

(

2022

)arethefirsttofocusontheroleofinstitutionalinvestorsincryptomarkets,andshowthattraditionalfinancialinstitutions,especiallylightlyregulatedbanks,arestartingtoholdcryptoassets.

9

Weusethisliteraturetohelpexplaintheco-movementbetweencryptoandequities,andtoconstructastylizedframeworkforinvestigatingpotentialspilloversbetweenthetwo.

Third,wecontributetotheliteratureontheglobalfinancialcycle.

10

Inherseminalcontribution,

Rey

(

2013

)showstheexistenceofasinglefactorthatexplains20%ofthevariationinglobalassetprices.Inmorerecentworks,

Miranda-AgrippinoandRey

(

2020

)and

Miranda-AgrippinoandRey

(

2021

)highlighthowUSmonetarypolicya↵ectsthisglobalfinancialcyclethroughtherisk-takingchannel.Achangeininterestratesforcesfinancialintermediariestochangetheirleverageandthusthee↵ectiveriskappetiteofthemarginalinvestor.AUSmonetarycontractionthusnegativelya↵ectsglobalequityprices,erodingtheindependenceofothercentralbanksandreinforcingthedominantroleoftheUSdollar(

PassariandRey

,

2015

;

FarhiandMaggiori

,

2018

).Withinthisliterature,weparticularlyfocusontherisk-takingchannelofmonetarypolicy.

Coimbraetal.

(

2022

)developacom-

8Similarly

Hackethal,Hanspal,Lammer,andRink

(

2021

)and

DidisheimandSomoza

(

2022

)documentthebehaviourofcryptoretailinvestorsandtheirportfolioallocationbetweenequityandcryptoassets.

9Nonetheless,banks’exposureremainslimitedwithrespecttothesizeoftheirbalancesheets.Inaddition,

Cornelli,Doerr,Frost,andGambacorta

(

2023

)documentdi↵erencesintradingbehaviourbetweensmallandlargeinvestorsduringcrisisepisodes.

10Forearlydiscussions,see:

Diaz-Alejandro

(

1985

);

Calvo,Leiderman,andReinhart

(

1996

).

8

prehensivedynamicmodelwithheterogeneousintermediaries,whichfeaturestime-varyingendogenousmacroeconomicrisk.Intheirframework,thevariationinrisk-aversionacrossagentsdeterminestheaggregateriskoftheeconomy.Relatedly,

AdrianandShin

(

2014

)high-lighthowthecyclicalityofleveragedependsontheconstraintsoffinancialintermediaries.

FostelandGeanakoplos

(

2008

)showhowleveragecyclescanbeexplainedbydi↵erencesinagents’beliefs,whereas

KekreandLenel

(

2018

)and

Gourinchas,Rey,andGovillot

(

2010

)focusonheterogeneityinriskaversion.Wecontributetothisliteraturebyincorporatinganalysisofthecryptocycle.

Therestofthispaperproceedsasfollows.Section

2

derivesthecryptofactor,thenSection

3

investigatesitsrelationshiptoequitypricesandtheglobalfinancialcycle.Section

4

examinestheimpactofUSmonetarypolicyonthecryptofactor,andSection

5

rationalizesourfindingsinaheterogeneous-agentmodel.Section

6

concludes.

2TheCryptoFactor

Thepricesofcryptoassetsarehighlycorrelated.Table

1

reportsthecross-correlationsamongthecryptoassetswiththelargestmarketcapitalization.Theseareremarkablyhigh,andmuchlargerthanthecorrelationsdocumentedacrossequitymarkets(see,forinstance,

Rey

,

2013

).Forexample,Bitcoinhasa52%averagecorrelationwithothercryptoassets.Wethusconjecturetheexistenceofacommoncryptofactorthatco-moveswithcryptoprices,inthesamespiritastheglobalequityfactorpioneeredby

Rey

(

2013

).

9

1.00

0.820.640.62

1.000.640.67

1.000.52

0.750.570.310.700.610.470.450.590.630.820.000.660.730.56

0.69

0.470.340.640.590.490.380.550.530.800.030.590.750.55

0.560.510.240.580.470.460.320.550.470.630.030.510.590.48

1.000.480.300.630.590.420.380.640.540.72-0.010.580.660.53

1.000.160.520.370.340.430.540.470.490.520.530.430.43

1.000.230.250.510.150.210.140.330.000.270.300.22

1.000.560.430.540.590.600.660.520.700.550.60

1.00

0.34

0.27

0.44

0.46

0.58

0.00

0.52

0.55

0.48

1.000.320.340.430.450.310.420.390.40

1.000.510.540.38-0.010.330.340.27

1.000.510.530.480.590.460.50

1.000.560.450.590.440.49

1.00-0.010.600.720.55

1.00

-0.01

0.04

-0.01

1.000.540.48

1.000.53

1.00

Table1:Correlationsamongcryptoassets

Bitcoin

EthereumBinanceCoin Ripple Cardano Solana DogecoinPolkadot

Tron ShibaInuMakerDaoAvalanche

Uniswap

Litecoin FTXChainlink MoneroTHETA

1.00

0.65

0.42

0.26

0.49

0.58

0.41

0.33

0.48

0.44

0.67

0.00

0.53

0.59

0.46

BitcoinEthereumBinanceCoinRippleCardanoSolanaDogecoinPolkadotTronShibaInuMakerDaoAvalancheUniswapLitecoinFTXChainlinkMoneroTHETA

Notes:Thistableshowspairwisecorrelationsbetweenselectedcrypto-assetreturns.DataisfromJanuary2018toMarch2023.

Tosummarizethefluctuationsincryptomarketsintoonevariable,weusedynamicfactormodelling,adimensionalityreductiontechnique.

11

Thisallowsustodecomposeasetofpricesintotheiridiosyncraticcomponentsandacommontrend.Specifically,westartwiththedailypricesofthelargestcryptoassetsthatwerecreatedbeforeJanuary2018(excludingstablecoins).Thisleavesuswithsevencryptoassets,accountingfor75%oftotalmarketcapitalizationinJune2022.

12

WethenwritethispanelofcryptopricespitasalinearcombinationofanAR(q)commonfactorftandanasset-specificidiosyncraticdisturbance✏it(whichinturnfollowsanAR(1)process):

pit=λi(L)ft+✏it

ft=A1ft−1+...+Aqft−q+⌘t

✏it=pi✏it−1+eit

(1)

⌘t⇠N(0,⌃)

eit⇠N(0,σ)

whereListhelagoperatorandλi(L)isaq-ordervectoroffactorloadingsforasseti.Estimatingthissystemusingmaximumlikelihood,selectingqusinginformationcriteria,

11Fortheevolutionofthemethod,see,amongothers:

Geweke

(

1977

);

SargentandSims

(

1977

);

Forni,

Hallin,Lippi,andReichlin

(

2000

);

BaiandNg

(

2002

);

StockandWatson

(

2002

);

Miranda-Agrippinoand

Rey

(

2020

).

12Theseare:Bitcoin,Ethereum,BinanceCoin,Ripple,Cardano,DogeCoin,andTron.

10

producesourcommonfactorft.

13

Itisalsopossibletospecifymultiplefactorsthata↵ectpricesdi↵erently,andweusethislatterspecificationwhenweconsidermultipledistinctsub-classesofcryptoassets.

Figure

1

showsthecryptofactorandtheunderlyingpriceseriesfromwhichweextractit.Thecryptofactore↵ectivelycapturesthesalientphasesthatcharacterizedcryptomarkets—suchasthedeclineatthebeginningof2018,thesubsequent‘cryptowinter’,thelatestboomwiththepeaksinBitcoinandDogecoin,andfinallytheslumpofTerraandFTXof2022—withoutbeingoverlyinfluencedbyisolatedspikeslikethoseofRippleandTron.

Figure1:Thecryptofactor

Notes:Thisfigureshowsthecryptofactor(blue)andthestandardizedcryptopricesfromwhichitisconstructed(grey)usingadynamicfactormodel.

Togaugetheimportanceofthisfactormoresystematically,weregresseachpriceseriesinturnonthecryptofactor.Onaverage,80%ofvariationintheunderlyingseriesisexplainedbyourcryptofactor.

14

Thisfigureisabove68%forallsevenassets,underscoring

13WeusethePythonpackagestatsmodels/DynamicFactor.Forfurtherinformationaboutthemodelandalgorithm,see

/dev/examples/notebooks/generated/statespace_

dfm_coincident.html

.

14SeeAppendixFigure

A.1

forthebreakdownacrossindividualcryptoassets.

11

thehighdegreeofco-movementoveroursampleperiod.Forcomparison,theglobalequityfactorcalculatedby

Miranda-AgrippinoandRey

(

2020

)explainsonly20%ofglobalequityprices,highlightingthegreaterco-movementandconcentrationofmarketcapitalizationinthecryptomarket.Ourfindingsthusstronglycorroboratethehypothesizedexistenceofasinglecryptofactorthatdrivesthepricesoftheentirecryptomarket.

Giventhelimitedrangeofassetsusedtocalculateourfactor,wealsoconfirmthatourcryptofactorreflectsmorerecenttrendsinnewerassets.

15

Todoso,weexamineabroadersampleofassets,groupedintofivecategories:FirstGenerationtokens(Bitcoin,RippleandDogecoin),SmartContractsplatformtokens(Ethereum,BinanceCoin,Cardano,SolanaandPolkadot),DeFitokens(Chainlink,Uniswap,MakerandAave),Metaversetokens(Flow,ApeCoin,theSandbox,DecentralandandThetaNetwork)andInternetofThingstokens(Helium,Iota,IoTexandMXC).Wethenestimateanewmodelwithfivedi↵erentfactors,whereeachfactorcanonlya↵ectoneclass.TheresultsareshowninFigure

2

,alongwiththegeneralcryptofactorestimatedabove.

16

Allclassesarehighlycorrelatedwiththegeneralcryptocycle,validatingourfocusonthecommontrend.

17

15Wedonotincludethesenewerassetsinthecalculationofthemainfactor,astheywouldfurtherlimitthetimespanofoursample.

16Notethatthetimespanforeachofthenewfactorsissubstantiallyshorter,giventhatmanywerecreatedonlyin2021.

17ThemainexceptionisthejumpintheMetaversefactorinlate2021,whenFacebookre-brandedtoMeta.Outsideofthisidiosyncraticshock,movementsintheMetaversefactoralsofollowthegeneraltrend.

12

Figure2:Cryptosub-factors

Notes:Thisgraphshowstheoverallcryptofactorandfivecryptosub-factors,standardizedandsmoothed.Thesub-factorsareconstructedfromthefollowingassets:FirstGenerationtokens—Bitcoin,RippleandDogecoin;SmartContractplatformtokens—Ethereum,BinanceCoin,Cardano,SolanaandPolkadot;DeFitokens—Chainlink,Uniswap,MakerandAave;Metaversetokens—Flow,ApeCoin,theSandbox,Decentra-landandThetaNetwork;andInternetofThingstokens—Helium,Iota,IoTexandMXC.

Finally,andconsistentwithanecdotalevidence,thecryptofactorcorrelateswithaproxyforleverageincryptomarkets.Figure

3

plotsthecryptofactoragainstcryptoleverage,definedusingthetotalvaluelocked(TVL)indecentralizedfinance(“DeFi”)contractsnor-malizedbytotalcryptomarketcapitalization.

18

Thisshowsthatthesystemwasrelativelyunlevereduntiltheendofthe2018-2019cryptowinter,afterwhichleverageincreasedsub