1
Positioning
Econometric
Methodology
Empirical
Banking
Economic
Theory
Empirical Banking
Results
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Positioning
Econometric
Methodology
Empirical
Banking
Economic
Theory
Empirical Banking
Results
3
Rules versus Discretion
Heteroscedastic Model
Cerqueiro, G., H. Degryse and S. Ongena,
2011, Rules versus discretion in loan rate
setting, Journal of Financial Intermediation,
20, 503-529.
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Who Makes the Credit Decisions?
Person or Machine Behind the Desk?
Are credit decisions identical?
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“Rules” versus “Discretion”
• “Rules” – a Computer that uses:
– A standardized pricing model
– Only objective criteria as inputs
 Predictable loan rates
• “Discretion” – a Loan Officer who may:
– Add subjective judgements as inputs
– Combine different inputs in any subjective way
– Make pricing mistakes
 Hard-to-predict loan rates
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This Paper
• Uses a heteroscedastic regression
model to assess the determinants of
the importance of “Rules” and
“Discretion” on contracted loan rates
– Loan rates should reflect some latent
combination of objective (“rules”) and subjective
(“discretion”) criteria
– Heteroscedastic model analyzes how the
predictive power of a linear loan-pricing model
changes with given firm, market and loan
characteristics
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Heterogeneity in Loan Pricing Models
• Sample split regressions (by loan size)
– From Degryse & Ongena (JF 2005)
– Specification: Loan Rate = Controls + Residual
Loan Size ($)
# Obs.
R2
Small (< 5,000)
5,850
0.01
Large (> 50,000)
1,850
0.67
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Econometric Model
• Heteroscedastic regression model:
yi = β'Xi + ui
Variance equation: σi = exp(γ‘Zi)
Mean equation:
• Extreme cases:
– “Rules”: R2 of mean equation → 1
– “Discretion”: R2 of mean equation → 0
• Model estimated by MLE (normality
assumption)
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Conclusions
• Heteroscedastic model identifies determinants of
unexplained dispersion of loan rates (“discretion”)
• “Discretion” increases with...
– Borrower opaqueness (Switching costs)
– Public information about the firm
• And decreases in...
– Loan size (Information search costs)
– Prime Rate
• “Discretion” has decreased over the last 15 years for
small loans to opaque firms
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Duration Analysis
Ongena S. and D.C. Smith, 2001, The
duration of bank relationships, Journal of
Financial Economics 61, 449-475.
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Motivation
• Theory suggests that the “special” role of a
bank arises through the firm-bank
relationship
• Learn more about the value of firm-bank
relationships
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Overview
• Collect annual data on “bank connections”
of Oslo Stock Exchange firms 1979-1995
• Estimate likelihood firm will end a bank
relationship
– conditional on duration of relationship.
– conditional on set of firm characteristics
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Overview
• Control for censoring in the data
• Censoring means that some important
information required to make a calculation
is not available to us. i.e. censored
– Cannot observe bank relationship before 1979
or after 1995
– Cannot observe bank relationship before listing
or after delisting
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Summary of Results
• No strong evidence of duration dependence
– Short relationships are as likely to end as long
relationships
– Some evidence of non-linear relationship
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Applications
• Whenever data is in the form of a
duration:
– Span between firm entry and exit
– Span in a particular status: e.g.
• Job Tenure
• Time before stock price reached a minimum
(max) threshold
• Time to sales take off
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Event Study Methodology
Ongena S., D. Michalsen, 2003, Firms and their
distressed banks: lessons from the Norwegian
banking сrisis, Journal of Financial Economics,
67.1, 81-112.
Do negative shocks to bank lending cause
declines in growth in real sector?
• YES. Bernanke (AER 1983), Bernanke and Blinder (AER
1988), Kahyap, Stein and Wilcox (AER 1993), Slovin, Sushka
and Polonchek (JF 1993), ...
• NO. Black (JFE 1975), King and Plosser (AER 1984), ...
• IT DEPENDS (on the financial system) Allen
and Gale (2000), Rajan and Zingales (JACF 1998, 2000), and
Greenspan (1999).
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This Study
• We take as our laboratory the near-collapse
of Norwegian banking system between
1988-91:
– Take first announcements of bank distress.
– Couple announcements with information linking publiclylisted firms to their banks.
– Conduct an event study of impact of bank distress
announcements on stock price of firms associated with
banks.
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Cumulative Abnormal Returns
rjt  α j  β j rmt 
20
γ
k  20
jk
δ jkt  ε jt
CAR(1,1)  ˆ j , 1  ˆ j ,0  ˆ j , 1
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Event Study Results
(Using a World Market Index, in %)
CAR window
Bank CAR
Firm CAR
(-1, +1)
-10.6**
-1.7**
(-3, +3)
-11.2*
1.5
(0, +10)
-9.6
0.9
(-10, -1)
0.1
-0.3
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Conclusions
• Present evidence from Norwegian banking
crisis suggesting publicly-listed firms are not
hurt when their bank becomes distressed.
• Evidence contrasts with studies from East
Asian countries, in particular Japan.
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Possible Explanations
• We miss informationally relevant dates or firms are squeezed
and switch before distress dates
• Negative and significant bank CARs
• Ultimate collapse was not credible, investors knew
Government would step in.
•
•
•
•
No bank failure since 1923
Period of deregulation: bank industry against intervention
Norion Bank: under administration, liquidated in 1989, some depositors loose
Press: uncertainty about bail-out, exact structure
• CRISIS LENGTH
• Publicly-listed firms versus other firms
• Other studies also focus on publicly listed firms
• LN SALES, AGE
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Conclusion Event Study
• Can be very useful if appropriate (and well
motivated)
• Can be linked with other empirical models
• But be aware of:
– pitfalls
– unpopularity in some quarters …
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Choice of Bank Nationality and
Reach
Nested Multinomial Logit
Berger A.N., Q. Dai, S. Ongena and D.C. Smith, 2003,
Тo what extent will the banking industry be globalized?
A study of bank nationality and reaсh in 20 European
nations, Journal of Banking and Finance, 27, 383-415.
Will the banking industry be
globalized?
• Anecdotal Evidence mixed:
– Yes. Barriers to competition and entry have fallen
dramatically.
– No. Few financial institutions are taking advantage of the
barrier reductions.
• Better question might be:
– Not when or if banking industry will become globalized,
– But the extent to which it will be globalized.
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Answering the question: Our
study
• Examines the provision of cash management
banking services to foreign affiliates of large
multinational corporations.
– “Cash management” refers to virtually all core banking
services (lending, deposit-taking, etc.), but on short-term
basis.
– “Foreign affiliates” refers to the operations of multinational
corporations outside the nation in which they are
headquartered.
– Sample includes over 2,000 affiliates operating in 20
European nations.
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Definitions
• Bank Nationality
– A host nation bank is headquartered in the nation in which
the affiliate operates.
– A home nation bank is headquartered in the nation of the
affiliate’s corporate headquarters.
– A third nation bank is headquartered outside the host and
home nations.
• Bank Reach
– A global bank provides services to sample firms in at least 9
of the 20 host nations and has at least $100 billion in assets
in 1995.
– A local bank provides services to sample firms only in the
nation of the bank’s headquarters.
– A regional bank is neither global nor regional.
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Multinomial Logit
(Unordered dependent variable
case)
Using logit models
for more than two outcomes.
• We could construct logit models to compare
dichotomous outcomes (e.g. “Host” = 1 or 0)
• Problem: we lose a lot of information, example:
if we do a logit on “Host” = 1 or 0, we may not
get a statistically significant effect of size if large
firms:
• are less likely to choose “Third” (“Host” = 0)
• and more likely to choose “Home” (“Host” = 0)
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Definition of a multinomial logit:
• In a multinomial logit model, we have a set of covariates that
predicts ln(p2/p1), ln(p3/p1), …
all at the same time.
where p1 , p2 , p3 , … refer to all possible outcome categories
and where p1 refers to the comparison category.
• Also called the simultaneous fitting approach
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The Tree of probabilities:
Total sample of foreign affiliates across 20 European nations
(2,118 firms)
Bank Nationality
Pr(host)
65.5%
(1,387 firms)
Pr(home)
17.7%
(374 firms)
Pr(global|
host)
Pr(regional|
host)
Pr(local| host)
20.5%
18.4%
61.1%
(255 firms)
(285 firms)
(847 firms)
Pr(global| home)
Pr(third)
16.8%
(357 firms)
Pr(global| third)
Pr(regional| third)
62.3%
Pr(regional|
home)
37.7%
63.3%
36.7%
(233 firms)
(226 firms)
(131 firms)
(141 firms)
Bank Reach
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Summary and Conclusions
Examine two dimensions of globalization:
bank nationality and bank reach.
• Show that firms, by far, prefer host nation banks to
home or third nation banks
– 2/3 host nation banks, 1/3 split between home and third nation
banks
• Choice of reach depends on choice of nationality.
• Very low levels of financial development (i.e. in
former socialist nations) imply lower usage of host
nation banks and stronger preference for global
banks
Are there limits
to the Globalization of the Banking Industry?
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