aurore burietz & loredana ureche-rangau iaes, milan – march 14, 2015

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?


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


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)


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)











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)











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


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


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!

aurore burietz & loredana ureche-rangau iaes, milan – march 14, 2015

Documents