aurore burietz & loredana ureche-rangau iaes, milan – march 14, 2015
TRANSCRIPT
Aurore Burietz & Loredana Ureche-Rangau
IAES, Milan – March 14, 2015
Bank lending characteristics&
The impact of the financial crisis
Home sweet home!
Motivation
Clarifying the impact of the subprime crisis on bank lending activities
Identifying credit supply determinants during crises
Supporting and improving banking regulation
Motivation Methodology Data Results
Research questions
Do we observe a home bias and/or a sectorial bias in bank lending after the collapse of Lehman Brothers?
What are the mechanisms/circumstances which affect the bank lending behavior and contribute to the home/sectorial bias?
How banks are different across countries?
Motivation Methodology Data Results
Summary What has been done?
Flight-to-home effect (Giannetti and Laeven, 2012)
What do we do? Joint estimation of loan’s amount and spread Introduction of sectorial bias Control for determinants of bank lending decisions Databases combination
What do we find? France & Germany: home bias – diversification Italy & Spain: home bias during the banking crisis
Methodology (1/4)
Two dependent variables Spread (LN(SPRD)) Amount (LN(AMT))
Joint estimation with 3SLS Credit supply equation Credit demand equation
Motivation Methodology Data Results
Methodology (2/4)
Determinants of credit terms FAC: loan’s characteristics BOR: company’s characteristics LEN: bank’s characteristics REL: relationship between the bank and the
company
Beim (1996), Hubbard et al. (2002), Calomiris and Pornrojnangkool (2005), Brick and Palia (2007), Chakravarty and Yilmazer (2007), Kapan and Minoiu (2013)
𝐿𝑁𝑆𝑃𝑅𝐷𝑖=𝛼𝑆+𝛽1𝑆 𝐹𝐴𝐶𝑖+𝛽2
𝑆𝐵𝑂𝑅𝑖+𝛽3𝑆𝐿𝐸𝑁 𝑖+𝛽4
𝑆𝑅𝐸𝐿𝑖+𝜃𝑆𝐺𝐸𝑂𝑖+𝜸
𝑺𝑺𝑼𝑷 𝒊+𝜹𝑺𝑳𝑵𝑨𝑴𝑻 𝒊+𝜀1 𝑖(1)
𝐿𝑁𝐴𝑀𝑇 𝑖=𝛼𝐷+𝛽1
𝐷𝐹𝐴𝐶 𝑖+𝛽2𝐷𝐵𝑂𝑅𝑖+𝛽3
𝐷 𝐿𝐸𝑁 𝑖+𝛽4𝐷𝑅𝐸𝐿𝑖+𝜃
𝐷𝐺𝐸𝑂 𝑖+𝜸𝑫𝑫𝑬𝑴 𝒊+𝜹
𝑫 𝑳𝑵𝑺𝑷𝑹𝑫𝒊+𝜀2 𝑖(2)
Motivation Methodology Data Results
Methodology (3/4)
Home bias variable GEO: nationality of the borrower AFTER
the collapse of Lehman Brothers
De Haas and Von Horen (2011), Cetorelli and Goldberg (2011), Giannetti and Laeven, (2012)
𝐿𝑁𝑆𝑃𝑅𝐷𝑖=𝛼𝑆+𝛽1𝑆 𝐹𝐴𝐶𝑖+𝛽2
𝑆𝐵𝑂𝑅𝑖+𝛽3𝑆𝐿𝐸𝑁 𝑖+𝛽4
𝑆𝑅𝐸𝐿𝑖+𝜃𝑆𝐺𝐸𝑂𝑖+𝜸
𝑺𝑺𝑼𝑷 𝒊+𝜹𝑺𝑳𝑵𝑨𝑴𝑻 𝒊+𝜀1 𝑖(1)
𝐿𝑁𝐴𝑀𝑇 𝑖=𝛼𝐷+𝛽1
𝐷𝐹𝐴𝐶 𝑖+𝛽2𝐷𝐵𝑂𝑅𝑖+𝛽3
𝐷 𝐿𝐸𝑁 𝑖+𝛽4𝐷𝑅𝐸𝐿𝑖+𝜃
𝐷𝐺𝐸𝑂 𝑖+𝜸𝑫𝑫𝑬𝑴 𝒊+𝜹
𝑫 𝑳𝑵𝑺𝑷𝑹𝑫𝒊+𝜀2 𝑖(2)
Motivation Methodology Data Results
Methodology (4/4)
Instruments SUP: determinants of loan supply, i.e.
government interventions and lender’s specialty
DEM: determinants of loan demand, i.e. sales growth and a variable for multi loans
Acharya et al (2006), Calomiris and Pornrojnangkool (2009)
𝐿𝑁𝑆𝑃𝑅𝐷𝑖=𝛼𝑆+𝛽1𝑆 𝐹𝐴𝐶𝑖+𝛽2
𝑆𝐵𝑂𝑅𝑖+𝛽3𝑆𝐿𝐸𝑁 𝑖+𝛽4
𝑆𝑅𝐸𝐿𝑖+𝜃𝑆𝐺𝐸𝑂𝑖+𝜸
𝑺𝑺𝑼𝑷 𝒊+𝜹𝑺𝑳𝑵𝑨𝑴𝑻 𝒊+𝜀1 𝑖(1)
𝐿𝑁𝐴𝑀𝑇 𝑖=𝛼𝐷+𝛽1
𝐷𝐹𝐴𝐶 𝑖+𝛽2𝐷𝐵𝑂𝑅𝑖+𝛽3
𝐷 𝐿𝐸𝑁 𝑖+𝛽4𝐷𝑅𝐸𝐿𝑖+𝜃
𝐷𝐺𝐸𝑂 𝑖+𝜸𝑫𝑫𝑬𝑴 𝒊+𝜹
𝑫 𝑳𝑵𝑺𝑷𝑹𝑫𝒊+𝜀2 𝑖(2)
Motivation Methodology Data Results
Data (1/2)
Syndicated loan market
European countries France Germany Italy Spain
Data (2/2)
Bank(s) Loan Company
Bankscope Dealscan Compustat, Orbis, Diane
Lender(s)
• Name(s)• Role• Bank
allocation
Loan
• Date• Amount• Currency• Spread• Benchma
rk• Type/obj.• Maturity• Secured
Borrower
• Name• Country• SIC code
Borrower
• Matching name
• Financial data (US $)
Lender(s)
• Matching name
• Financial data (US $)
• History (mergers…)
Bor. IDLoan ID
Motivation Methodology Data Results
Loan supply regression (LNSPRD)France Germany Italy Spain
Coef Std Err Coef Std Err Coef Std Err Coef Std ErrCFR 0.3156 0.1144*** 0.1104 0.1560 0.2996 0.1904 0.1130 0.2062
CGE 0.4747 0.1271*** 0.4372 0.1290*** 0.5742 0.1904*** 0.1639 0.2142
CIT 0.3663 0.1901* 0.9650 0.2611*** 0.8164 0.2231*** 0.6269 0.2665**
CSP 0.9512 0.1042*** 0.9508 0.1307*** 0.9142 0.1599*** 0.5162 0.1375***
CEUR 0.5586 0.0981*** 0.4400 0.1083*** 0.5289 0.1562*** 0.4666 0.1666***
CNOAM 0.4605 0.0691*** 0.6257 0.0752*** 0.5628 0.1200*** 0.4701 0.1796***
CASIA 0.5920 0.0725*** 0.6787 0.0954*** 0.5251 0.1634*** 0.3646 0.2736
Results (1/4) – GEO (2008-2013)
Significant Home Bias
(Wald coefficient test)Motivation Methodology Data Results
Results (2/4) – GEO (2 crises)
Coef Banking C Sovereign debt C Banking C Sovereign debt C France (R² = 0.6116) Germany (R² = 0.6369)
CFR 0.1063 0.4526*** 0.0774 0.1047CGE 0.4377** 0.5094*** 0.4582** 0.4121***CIT -0.1848 0.8902*** 1.0428** 0.9550***CSP 0.4098** 1.2066*** 0.4538*** 1.1199***CEUR 0.4042*** 0.6980*** 0.3193** 0.5142***CNOAM 0.5005*** 0.4573*** 0.6734*** 0.5178***CASIA 0.3620*** 0.8282*** 0.7085*** 0.6487***
Italy (R² = 0.6620) Spain (R² = 0.7252)CFR 0.2149 0.1542 0.3786 -0.0229CGE 1.0589*** 0.2484 0.4681 -0.2571CIT 0.5324* 1.0364*** 0.3739 0.8108**CSP 0.5636** 1.1038*** 0.3350* 0.5845***CEUR 0.6943*** 0.2190 0.3753* 0.4405**CNOAM 0.9046*** 0.1210 0.9064*** -0.1246CASIA 0.4580** 0.5216** 0.4413 0.3099
Results (3/4) – Sectorial bias
Loan supply regression (LNSPRD)France Germany Italy Spain
Coef Std Err Coef Std Err Coef Std Err Coef Std ErrCLS 0.0132 0.0025*** 0.0064 0.0031** 0.0037 0.0044 -0.0010 0.0039
Banking C Sovereign debt C Banking C Sovereign debt CCoef Std Err Coef Std Err Coef Std Err Coef Std Err
France GermanyCLS 0.0172 0.0034*** 0.0095 0.0034*** 0.0130 0.0043*** 0.0013 0.0034
Italy SpainCLS 0.0040 0.0062 0.0028 0.0027 0.0028 0.0064 -0.0040 0.0042
Motivation Methodology Data Results
Results (4/4) – FAC, BOR, LEN, REL FAC
Higher spread for secured loans Lower spread for loans in euro
BOR Lower spread for borrowers with a good
financial position (high level of assets, low level of long-term debt)
LEN & REL Better credit terms when…▪ … banks have strong financial position▪ … previous relationship existsMotivation Methodology Data Results
Conclusion
Home bias for all banks
Portfolio diversification for French and German banks
Strong implications for banking regulations
Aurore Burietz & Loredana Ureche-Rangau
IAES, Milan – March 14, 2015
Bank lending characteristics&
The impact of the financial crisis
Home sweet home!