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OutlineFinancialcrises,intermediation:• Whatcanwelearnaboutassetpricing?– Muir2017,QJE– AdrianEtulaMuir2014,JF– HaddadMuir2017
• Whatcanwelearnaboutcreditcyclesandmodelsofcrises/frictions?– KrishnamurthyMuir2017
• Broadempiricalfacts– Usefulforcalibrationandsortingoutmodels
Data1870-2010across14countries• Assetprices:creditspreads,d/p,stockreturns– Manysources,discussspreadslater
• Macro:consumption,GDPfromBarro andUrsua• Financialcrisisdates:Schularick andTaylor,ReinhartRogoff– …wedefinefinancialcrisesaseventsduringwhichacountry'sbankingsectorexperiencesbankruns,sharpincreasesindefaultratesaccompaniedbylargelossesofcapitalthatresultinpublicintervention,bankruptcy,orforcedmergeroffinancialinstitutions….
Background:Learningaboutassetpricing
• Bigquestion,whydoriskpremia E[R]movesomuchovertime?
• GordonGrowth
• Expectedreturn,E[r],must beveryvolatile
grpd −=/
Volatileprices
Smoothcashflows
Why?• Theories:whydoexpectedreturnsvarysomuch?
• Standard:repagent(household)– Habits(S=surplusconsumption),LRR(S=vol(c)),Raredisaster(S=prob disaster)
– Behavioral:S=extrapolation,sentiment
• Intermediarymodels– S=healthoffinancialsector,“riskbearingcapacity”
Howcanwedistinguish?
• LookforepisodeswhereS shouldhavemovedaccordingtorepagentmodels– Recessions,wars
• Comparetoepisodeswherebankingsystem andcreditwereadverselyaffected
• Historicaldata14countries1870-2010– Financialcrises(=bankruns),recessions,deeprecessions,wars
Result• Riskpremia appearlargestinbankingcrises
0
0.1
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0.7
0%
5%
10%
15%
20%
25%
30%
Financial Crises Recessions Deep Recessions Wars
Change in d/p Change in Credit Spread
Result• Consumptionstatevariablesdon’tmatchvariation
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0.02
0.04
0.06
0.08
0.1
0.12
0.14
0%
5%
10%
15%
20%
25%
30%
Financial Crises Recessions Deep Recessions Wars
Decline in Consumption Consumption Volatility
Interpretation• Explanationforspikesinriskpremia can’tonlyrelyonmacroeconomydoingpoorly
• Suggestive:credit/healthofbankingsystemareimportantforassetprices
• “Suggestive”ofintermediarytheories,butcanwemoreaccuratelymeasureSandtest?
Intermediarypricingkernel• Assetpricingequation
– M capturesmarginalutility(V’(W)),marginalvalueofadollar
– OftentrytomeasureV’(W)fromhousehold– Intermediarytheory:useV’(W)ofintermediary
IntermediarypricingkernelHowtomeasureV’(W)ofintermediary?• Theory:Brunnermeier Pedersen(2009)
• Idea:badtimes,constraintstighten,intermediariesdelever,V’(W)ishigh• AEM(2014):logchangeinleverageofbroker-dealers,flowoffundsquarterly1969-2009– Largedeleveragingincrisis
Identification• The“veil”hypothesis:intermediariesjustreflectmarginalutilityofHHbutdon’tactuallymatter
• Howcanwetest?• HaddadandMuir2017– Keypredictionof“frictionless”viewoftheseresultsisthatallriskpremia increaseproportionallytoriskaversionshock
– Alternative:relativeriskpremia elasticities shouldbelargerinmoreintermediatedassetclassesifthereisanintermediaryriskaversionshock
Conclusions• Suggestive:creditconditions/healthofbankingsystemareimportantforassetprices
– Separatedthiseffectfromrepagentbeingaffectedbybadmacroeconomicshock
– Morework• Spellingoutthefrictions(multipleintermediarymodels),calibratingmodels• Empiricalworkonidentificationvsfrictionlessviews• Connectmicrostudies(MitchellPulvino2010,DuTepper Verdelhan2017,etc)tomacro
Wedescribethebehaviorofoutput,credit,andcreditspreadsarounda
financialcrisis• Whatisafinancialcrisis?
– IsacrisisjustabadTFPrealization?• Datathroughthelensof“𝐹"×𝑧"”model
– 𝐹" isfinancialsectorfragility(“amplifier”)– 𝑧" isshock(losses)tothefinancialsectorbalancesheet(“trigger”)
• Mainresults:1. Crisesareassociatedwithlargeunexpectedlossestothefinancial
sector2. Fragilityandsizeoflossessummarizethesubsequentoutputdecline3. Pre-crisis,therunup isdrivenbyacreditsupplyexpansion:spreads
appeartobe“toolow”
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EmpiricalStrategy
• Whathappensaroundafinancialcrisis?
• Approach:– Defineasetofdatesidentifiedwithamajorfinancialcrisis
– Examinethebehaviorofoutput,credit,andspreadsaroundthesedates
– Comparetonon-crisisevents
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Theory:Whatisafinancialcrisis?
• Shock𝑧" :recessionaryshock,lowerexpectedcash-flowsonassetsheldbyintermediaries
• Fragility𝐹" :highleverage/lowequitycapital,short-termdebt,correlatedintermediarypositions,interconnectedexposures
• “Trigger”+“Amplification”– Assetpricefeedback– Creditcrunch– Bankruns/failures/disintermediation
• Creditspreadsrise:– Expecteddefault+risk/illiquiditypremium
• Kiyotaki-Moore,He-Krishnamurthy,Brunnermeier-Sannikov,BernankeGertler others
24
Quantitative• Identifyhighfragility(𝐹")
• Identifylargelosses(𝑧")
• Definecrisesaseventswithlargelosseshittingfragilefinancialsector
• Sidebenefit:avoids“weknowonewhenseeone”critiqueofthenarrativeapproach (e.g.,Schularick Taylor,ReinhartRogoff)
25
Data:Creditspreads,crisisdates,GDP
• 1869-1929across14countriesfromoldnewspapers• 1930-presentfromvariouscentralbanksandotherdatasets
(Datastream,GlobalFinancialDatabase)formorerecentcreditspreads– Highgrademinuslowgradecorporatespread– Corporatebondindextogovernmentbond
• Wenormalizeeachcountry’sspreadas:
𝑠&," = 𝑠𝑝𝑟𝑒𝑎𝑑&,"𝑠𝑝𝑟𝑒𝑎𝑑&
• Totalof900country-yearobservations
26
Creditspreads:1869-1929
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• Individualbondpricesonbanks,sovereigns, railroad,etc.• Over4000uniquebonds, 200,000bond/years
• Weconverttoyieldtomaturity• Spread=high10th percentileavg yieldminus low10th percentileavg yield
Data:Creditspreads,crisisdates,GDP
• Wecrossthisdatawithcrisis-recessiondatesfromSchularick-Taylor(ST)andnon-financialrecessionfromST.– Robustness:Reinhart-Rogoff(RR),Bordo-Eichengreen-Klingebiel-
Martinez(BE)
• Totalof900country-yearobservations:– 44STcrises– 48RRcrises– 27BEcrises
• GDPdatafromBarro-Ursua
28
Specification
• Paneldataregressions(country𝑖,horizon𝑘):• Interactspreadswithcrisisdummies
ln𝑦& ,"45𝑦& ,"
= 𝑎& + 𝑎" + 189&:&:×[𝑏8×𝑠&," + 𝑏=>8 ×𝑠&,"=>]+
1@A=89&:&:×[𝑏@8×𝑠&," + 𝑏=>@8×𝑠&,"=>]+ 𝑐′𝑥" + 𝜖&,"45
• Controls: laggedGDPgrowth,3yearcreditgrowthfromSchularick-Taylor
• Standarderrorsclusterbycountry
31
Pre-crisisbehavior
41
• Schularick andTaylorfact:Crisesprecededbycreditgrowth
• Newfact:Crisesalsoprecededbyfallingcreditspreads
• Explanations:
1. (Overoptimistic)incomeprospectsdrivesborrowing/lending
2. Financialsectorriskpricingandriskpremia fall
3. Note:notpredictedbyexistingintermediarybasedmodels/rationalmodelsofcreditboombust• SeealsoBaronXiong (2017)
MarginalprobabilitiesofSTcrisisfromProbit
44
00.10.20.30.40.50.60.7
1 2 3 4 5
P(Crisis)
Year
P(Crisis|HC_HF=0) P(Crisis|HC_HF=1)
Summary• Spikesinspreads+realfragility=Losses+Amplification– LeadtopoorGDPoutcomes(Kiyotaki-Moore,He-Krishnamurthy)
• Crisesareprecededbyunusuallylowspreads– Spreadspre-crisisdonotpriceanincreaseinfragility– Creditsupplyexpansionsprecedecrises– “Surprise”isakeydimensionofcrises
• Aftermathoffinancialcrisesisdeeprecession– Weusevariation inseverityindexedbyspreads– ResultsconsistentwithReinhart-Rogoff,Schularick-Taylor;
– Wegivemorepreciseanswers,usefulforcalibratingmodels
48
Conclusions• Recentinterestinmacromodelsofintermediation,assetprices,crises
• Stylizedempiricalfactsappearpromisingforthesetheories
• Someshortcomingsaswell(e.g.,riskpremialowbeforeacrisis)
• Quantitativepatternsfortheoriestotarget• Moretobedoneonunderstandingmechanismsoffrictions,identificationvsfrictionlessmodels