Domestic banks as lightning rods? home bias and ......I De Marco and Macchiavelli (2015) I Becker...
Transcript of Domestic banks as lightning rods? home bias and ......I De Marco and Macchiavelli (2015) I Becker...
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
Domestic banks as lightning rods?Home bias and information
during Eurozone crisis
Orkun SakaLondon School of Economics and Political Science
March 29, 2019ECB-JMCB Joint Conference on
Financial Intermediation, Regulation and Economic Policy
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
SOVEREIGNS AND BANKS IN EUROZONE
I Deathly loop between sovereign and bank credit risksI Acharya, Drechsler and Schnabl (2014, JF)
I A silver lining for the link between governments anddomestic banks?
I Large literature on the role of distance in banks’ lendingbehaviour (Mian, 2006 JF)
I “Daily exposure to local news stories, firsthand knowledgeof the local economy, and personal relationships with keypeople at the issuing body” (Butler, 2008 RFS)
I First study showing that soft information matters for banks’sovereign bond exposures
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BANKS’ HOME BIAS
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MOTIVATION
Why do we see rising home bias in crisis-country banks?
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LITERATURE: MORAL SUASION
I Governments in difficulty pressuring domestic banks
I High correlations between “government relatedness” anddomestic sovereign bond holdings
I De Marco and Macchiavelli (2015)I Becker and Ivashina (2018)I Ongena, Popov and Van Horen (2019)
I This paper: information channelI Evidence from private debtI Evidence from foreign banks’ sovereign exposuresI Evidence from non-Greek exposuresI Evidence from post-crisis episode (2013-2015)
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
LITERATURE: SECONDARY MARKETS
I Governments are less likely to default if debt is held bydomestic agents
I Broner, Martin and Ventura (2010, AER)I Gennaioli, Martin and Rossi (2014b, JF)
I Empirical support is limited:I Banks in 191 countries holding more government securities
before/during crises (Gennaioli, Martin and Rossi, 2014a)I Crisis-country government debt has been reallocated to
banks in politically more influential Euro countries (Bruttiand Saure, 2016, JEEA)
I This paper:I No sign of rising home bias for other domestic non-bank
agentsI Use of home country political strength as a control
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
LITERATURE: SECONDARY MARKETS
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LITERATURE: RISK-SHIFTING
I Weakly-capitalised banks may prefer high-yielding riskyassets
I Acharya and Steffen (2015, JFE)I Horvath, Huizinga and Ioannidou (2015)
I Are all weakly capitalized banks located incrisis-countries?
I Weak-capitalization might be a government choice(Crosignani, 2015)
I This paper:I Weak evidence for risk-shifting in generalI Use of risk-shifting as a control
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
THIS PAPER: INFORMATIONAL ASYMMETRIES
I One of the most conventional (albeit lately-forgotten)theories of home bias in asset markets
I Assumption: Domestic agents have larger initialinformation endowments relative to foreigners
I Brennan and Cao (1997, JF): trend-following behaviour offoreign agents
I Van Nieuwerburgh and Veldkamp (2009, JF): endogenousand costly information acquisition
I Dziuda and Mondria (2012, RFS): sophisticated fundmanagers with locally-biased investors
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
THIS PAPER: INFORMATIONAL ASYMMETRIES
I Empirical evidence on informational-distanceI Coval and Moskowitz (1999, JF; 2001, JPE): geographical
proximity within USI Grinblatt and Keloharju (2001, JF): physical location,
culture, language within FinlandI Hau (2001, JF): location and language for German stocksI Portes and Rey (2005): geographical distance proxying
bank branches, telephone and tourist traffic
I This paper:I Constructing similar information proxiesI Extending the evidence to banks’ government bond portfolios
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
BANK BRANCH NETWORK IN EUROPE
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EBA DISCLOSURES
I A rare dataset with banks’ actual government bondholdings (unlike Bankscope, SNL or BIS)
I 147 banks: covering 65% of total banking assets in EEAand 50% in each member country
I Comparison with Altavilla et al. (2017) & Ongena et al.(2019)
I Less frequency (biannual vs monthly)I Finer granularity (full country-breakdown vs
domestic/foreign dichotomy)I Wider sample of banks (including non-Eurozone)
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
DEPENDENT VARIABLE
I Main variable of interest:
SovereignPortionb,c,t =NominalExposureb,c,t∑
b
NominalExposureb,c,t
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ALTERNATIVE DEPENDENT VARIABLE
I CAPM correction (Coeurdacier & Rey, 2012, JEL):
SovereignPortionCAPMb,t =
∑c
NominalExposureb,c,t∑b,c
NominalExposureb,c,t
Biasb,c,t =SovereignPortionb,c,t − SovereignPortionCAPMb,t
1 − SovereignPortionCAPMb,t
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OTHER VARIABLES
I To include private forms of debt:DebtPortiond,b,c,t(SovereignPortionb,c,t & RetailPortionb,c,t)
I To include non-bank private residents:DomesticPortionc,k,t(ResidentBanksk & OtherResidentsk)
I Domestic dummy: Domesticl,cI Crisis dummy: Crisisc,t (spreads > 400bps & Euro)
I StressedBankl,t
I Information variables:I Direct: Branchesl,c, Mergersl,c, Pressl,c, Languagel,cI Indirect: Distancel,c, Borderl,c, Colonyl,c, Legall,c
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SUMMARY STATISTICS
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SOME SIMPLE FINDINGS
Are existing theories satisfactory enough?
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
RISE IN HOME BIAS
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
RISK-SHIFTING & HOME BIAS
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
BANK VS NON-BANK RESIDENTS
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
PUBLIC VS PRIVATE DEBT
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IDENTIFYING THE INFORMATION CHANNEL
Baseline Model:
SovereignPortionl,b,c,t = β1(SovereignRiskc,t × Informationl,c)
+θb,t + γc,t + µl,c + εl,b,c,t
I Two layers:1. Informational distance measured directly and indirectly2. Sovereign risk measured through bond spreads
I This strategy helps me control for µl,cI Controlling for country-specific average “Home Bias”I Controlling for all constant bilateral relationshipsI Identification mainly via time variation in spreads
I Conservative approach: focusing on foreign banks
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
INFORMATION CHANNEL (DIRECT)
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
INFORMATION CHANNEL (INDIRECT)
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INFORMATION CHANNEL (DIRECT)
Controlling for risk-shifting and political strength:
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INFORMATION CHANNEL (DIRECT)
Controlling for RS+PS / Eurozone banks:
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
INFORMATION CHANNEL (DIRECT)
Controlling for RS+PS / Eurozone banks / No exposures to Greece:
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
INFORMATION CHANNEL (DIRECT)
Controlling for RS+PS / Eurozone banks / No exposures to Greece /No Greek banks:
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
INFORMATION CHANNEL (DIRECT)
Post-crisis period:
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ROBUSTNESS CHECKS
I Combining all sub-sample restrictions
I Re-defining the dependent variable:I SovereignPortionBiasl,b,c,tI Log(1 + NominalExposure)l,b,c,tI SovereignPortionECBl,b,c,t
I Different crisis definitions:I Thresholds (300bps, 500bps)I Fast-moving crisis (1-month rolling yields)I Bond spreadsI CDS spreads
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IMPLICATIONS
I How much does information matter for Europe?I Initial home bias & costly information acquisition (Van
Nieuwerburgh and Veldkamp, 2009, JF)I Panic and market overreaction in the Eurozone (De
Grauwe and Ji, 2013; Saka et al., 2015)
I Policy advice:I Too much emphasis on blaming governments/banksI Regulatory changes or debt pooling: sufficient?I More transparency & cross-border banking
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CONCLUSION
I Using a novel dataset, this paper challenges the alternativearguments of the recent literature
I Debt is reallocated to domestic banks at the peak of thecrisis
I Risk-shifting contributes to rising home bias but its effect isnegligible in size
I No rising home bias for domestic non-bank agentsI Private forms of debt (at least) equally suffer from rising
home biasI The paper shows for the first time that informationally-closer
foreign banks increase their relative exposures as sovereign riskrises
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INTRODUCTION DATA & METHODOLOGY RESULTS CONCLUSION
THANKS FOR YOUR ATTENTION!