multinational banks - boston universitypeople.bu.edu/garettos/fgg_nyfed_12_13_new.pdf ·...

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Multinational Banks ∗

Jose L. FillatFederal Reserve Bank of Boston

Stefania GarettoBoston University

Martin GotzGoethe Universitat

December 4, 2013

∗The views expressed in this paper are the authors’ only and not those of the Federal Reserve Bank of Boston orthe Federal Reserve System.

Introduction

• Objective

• Literature

Data

Model

Calibration

Results

Conclusions

2 / 32

The Boston Globe, October 26th 2013

“Spanish-based Santander (...) acquired Sovereign Bank in 2009 as thespringboard for its US ambitions, [establishing] 700 branches and ATMsacross nine northeastern states.”

“Santander is the fourth-largest bank by deposits in Massachusetts andhas 1.7 million US customers. Emilio Botin, chairman of the parentcompany, said last week during a visit to the United States that he hopes tosee profits for the American business double in three years to $2 billion.”

This Paper

Introduction

• Objective

• Literature

Data

Model

Calibration

Results

Conclusions

3 / 32

We develop a structural model of entry in the foreign banking market .

• The model is a “good description” of the foreign banking sector in theUS:

◦ assumptions motivated by institutional details of the sector;◦ model designed to replicate empirical patterns on the activities of

foreign banking institutions in the US:

• differences in presence, size and activities of branches versussubsidiaries of foreign institutions.

• Structural model is amenable to counterfactual analysis to study:

◦ the risk implications of foreign banking;◦ the efficiency properties of the regulation in place.

Related Literature

Introduction

• Objective

• Literature

Data

Model

Calibration

Results

Conclusions

4 / 32

• Empirical analysis of foreign banking:

Goldberg (2007, 2009), Cetorelli and Goldberg (2010, 2012 JF andAER PP)

• Models of Trade and FDI in the Banking Sector:

Eaton (1994), De Blas and Russ (2012), Niepmann (2012, 2013),Bremus et al. (2013)

• To build our model:

◦ Micro-founded Models of Banking:

Klein (1971), Monti (1972)

◦ Models of investment under uncertainty:

Dixit (1989), Fillat and Garetto (2012)

Data Description and Sources

Introduction

Data

• Mode of Entry

• Flows

• Summary Statistics

• Size Differences

• Portfolio Composition

• Summary

Model

Calibration

Results

Conclusions

5 / 32

• Regulatory reporting data filed by US domestic banks, US subsidiariesof foreign banks, and U.S. branches/agencies of foreign banks (CallReports - FFIEC 002, 031,041)

• Foreign owned institutions :

◦ U.S. branches and agencies of foreign banks, and◦ U.S. banks of which more than 25% is owned by a foreign banking

organization or where the relationship is reported as being acontrolling relationship.

• Sample period: 1995-2010.

⇒ History of Banking Regulation

How do Foreign Banks Enter the US Market?

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

6 / 32

Home Country Foreign Country

BHC

BHC

Branch Subs

BHC

Subs

Offices Offices

Subs

BHC

BHC

Subs

Subs

Bank

Offices Offices

Subs

Offices Offices

Offices Offices

Offices Offices

Subs

Offices Offices Offices Offices

Domestic institutions

FBOs

Example: Royal Bank of Scotland

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

7 / 32

U.S. U.K.

CFG

BofA,

Corp

RBS

Securities

BofA

BHC

BofA

NA

M.L.

RBSG,

Plc

RBS,

Plc

RBS

Bank

Lloyd’s

Bank of

Utica

Offices Offices

Citizens

Offices Offices RBS

Fund

Offices

Domestic institutions

FBOs

RBS

Int’l

Bank of

Scotland

NY Branch

How do Foreign Banks Enter the US Market?

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

8 / 32

• Subsidiary Banks :

◦ 64 banks, total assets approx $1tn;◦ subject to US regulation and capital requirements;◦ give loans and accept both wholesale and retail deposits (with

deposit insurance).

• Branches and Agencies :

◦ 215 branches and agencies, total assets approx $2tn,;◦ subject to US regulation but NOT to capital requirements;◦ give loans and accept only wholesale deposits (they cannot accept

insured deposits);◦ display large intrafirm flows with the foreign parent.

• Other : Edge and Agreement Corporations, Representative offices andNon-depository trusts.

Foreign Banking Institutions: Total Flows

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

9 / 32

1015

2025

30S

hare

1995q4 1998q4 2001q4 2004q4 2007q4 2010q4

% of Foreign C&I Loans

1015

2025

30S

hare

1995q4 1998q4 2001q4 2004q4 2007q4 2010q4

% of Foreign Loans

1015

2025

30S

hare

1995q4 1998q4 2001q4 2004q4 2007q4 2010q4

% of Foreign Total Assets

1015

2025

30S

hare

1995q4 1998q4 2001q4 2004q4 2007q4 2010q4

% of Foreign Deposits

Foreign Banking Institutions: Summary Statistics

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

10 / 32

Mean Std. Dev. Median N. obs.

AssetsDomestic 1,649.91 33,640.07 147.62 6934Foreign Subsidiary 15,270.86 35,613.84 1,314.64 64Foreign Branch 8,892.19 19,548.42 803.33 215

DepositsDomestic 1,160.49 23,003.66 123.82 6934Foreign Subsidiary 11,006.95 26,373.87 985.61 64Foreign Branch 5026.401 11990.65 299.34 215

LoansDomestic 940.6878 16038.81 93.389 6934Foreign Subsidiary 8092.347 17701.04 748.5415 64Foreign Branch 2215.568 5411.098 345.288 215

[Numbers are in $ bn, year 2010. Data source: Federal Reserve Board ofGovernors, U.S. Share Data for U.S. Offices of Foreign BankingOrganizations.]

Size Differences: Deposits

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

11 / 32

05

10bn

$

1995q4 1997q4 1999q4 2001q4 2003q4 2005q4 2007q4 2009q4

foreign−branch foreign−subsidiary

domestic bank

Average Deposits

⇒ Size distributions

Size Differences: Loans

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

12 / 32

02

46

8bn

$

1995q4 1997q4 1999q4 2001q4 2003q4 2005q4 2007q4 2009q4


domestic bank

Average Loans


Size Differences: Assets

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

13 / 32

05

1015

bn $

1995q4 1997q4 1999q4 2001q4 2003q4 2005q4 2007q4 2009q4

foreign−branch foreign−branch (+due from related institutions)

foreign−subsidiary domestic bank

Average Assets


Intra-firm Flows

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

14 / 32

−1

01

23

bn $

1995q1 2000q1 2005q1 2010q1

Net Due From Head Office Net Due To Head Office

Intrafirm Balances (Average)

Portfolio Composition: Loans-to-Assets Ratio

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

15 / 32

.2.3

.4.5

.6bn

$

1995q4 1997q4 1999q4 2001q4 2003q4 2005q4 2007q4 2009q4


domestic bank

Loans / Assets

Portfolio Composition: Loan Types

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

16 / 32

020

4060

8010

0

Domestic Bank Foreign−Subsidiary Foreign−Branch

Loan Portfolio

Commercial & Industrial Real Estate

Other

What to Take Away

Introduction

Data

• Mode of Entry

• Flows




• Summary

Model

Calibration

Results

Conclusions

17 / 32

• Foreign banking in the US is a large phenomenon.

• Foreign banks are larger than domestic incumbents: evidence ofselection .

• Subsidiaries of foreign banks are larger than foreign branches , andmore similar to the domestic incumbents in their activities.

• Foreign branches appear to be a source of funding to their parents.

A good structural model of foreign banking must be consistent with thesefacts.

The Environment of the Model

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

18 / 32

• Two countries, Home and Foreign (denoted by ∗).

• Time is continuous.

• Each country is populated by a large mass of national banks:

◦ each bank offers one-period loans (L), makes investments (I) and acceptsdeposits (D);

◦ each bank has some market power in the loans market (start withmonopolistic competition to rule out strategic considerations).

• Study the decision of banks from the Home country to enter the Foreigncountry:

◦ each bank enters if it can make positive profits in the Foreign country;◦ the rationale for entry is given by differentiation (spatial or product);◦ a bank can enter a market either as a branch or as a subsidiary.

The Environment of the Model (contd.)

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

19 / 32

• Banks are heterogeneous in their ability a of managing loans,investments and deposits:

◦ Each bank has management costs a · C(D,L, I), where C(D,L, I) is aconvex function;

◦ When a bank enters the Foreign market, it transfers his efficiency a to thesubsidiary or branch.

• There are sunk costs of entry, depending on the organizational form ofthe foreign affiliate: Fs > Fb > 0.

• Loans and investments are risky (on aggregate).

⇓The solution of the optimal entry problem is a bank-specific policy functionthat determines a bank’s entry decision and mode of entry as a function ofbank-level characteristics and aggregate variables (aggregate loan demandand return on investments).

Why THIS model?

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

20 / 32

• Broader research agenda on the risk implications of foreign activities,building on Fillat and Garetto (2012), Fillat, Garetto and Oldenski(2013):

◦ aggregate, country-specific shocks and sunk costs of entry generatehysteresis in firms’ decisions;

◦ entry after a series of positive shocks may not be followed by exitwhen shocks revert (Dixit 1989, Baldwin and Krugman 1989);

◦ possible “optimal losses” are a source of risk to the firm.

• This model:

◦ allows us to quantify the risk arising from banks’ foreign activitiesassociated with different kinds of shocks;

◦ can be used to perform counterfactual exercises where we modifyinstitutional features of the sector and evaluate their consequencesfor risk exposure.

Why THIS model?

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

20 / 32

• Broader research agenda on the risk implications of foreign activities,building on Fillat and Garetto (2012), Fillat, Garetto and Oldenski(2013):

◦ aggregate, country-specific shocks and sunk costs of entry generatehysteresis in firms’ decisions;

◦ entry after a series of positive shocks may not be followed by exitwhen shocks revert (Dixit 1989, Baldwin and Krugman 1989);

◦ possible “optimal losses” are a source of risk to the firm.

• This model:

◦ allows us to quantify the risk arising from banks’ foreign activitiesassociated with different kinds of shocks;

◦ can be used to perform counterfactual exercises where we modifyinstitutional features of the sector and evaluate their consequencesfor risk exposure.

• What we CANNOT talk about:

◦ Maturity mismatch◦ Drivers of aggregate shocks in the banking sector.

Intra-temporal Problem: National Banks

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

21 / 32

Determine the optimal per-period profits of a bank given its foreign status.

• A national bank chooses the optimal amounts of loans L, deposits D,investment I , interbank borrowing M , and equity E to maximize itsprofits πN :

maxL,I,D,M,E

πN = prL(L) · L− (1− p)L+ rII − rDD − rMM − ...

aC(D,L, I)− fp ·D

s.t. M +D + E = L+ I (resource constraint)

E

ωLL+ ωII≥ k (capital requirement).

where p is the probability of loan repayment, fp is the deposit insurancepremium, k is the capital requirement, and ωL, ωI are weights.rL(L) is a downward-sloping demand for loans, while rI , rD, and rM aretaken as given by the bank.

Intra-temporal Problem: Parent + Foreign Subsidiary Pair

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

22 / 32

• In a parent + subsidiary pair, the profit maximization problem of theparent in the home country is identical to the problem of a nationalbank. The foreign subsidiary is operated as an independent entity tomaximize its profits πS :

maxL∗,I∗,D∗,M∗,E∗

πS = pr∗L(L∗) · L∗ − (1− p)L∗ + rII

∗ − rDD∗ − ...

rMM∗ − aC(D∗, L∗, I∗)− fp ·D∗ − FS

s.t. M∗ +D∗ + E∗ = L∗ + I∗ (resource constraint)

E∗

ωLL∗ + ωII∗≥ k (capital requirement).

Intra-temporal Problem: Parent + Foreign Branch Pair

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

23 / 32

• Due to the possibility of internal transfers between a parent and aforeign branch, we solve their problems jointly.

maxL,I,D,M,E

L∗,I∗,D∗,M∗,T

prL(L) · L− (1− p)L+ rII − rDD − rMM − ...

aC(D,L, I)− fp ·D + ...

pr∗L(L∗) · L∗ − (1− p)L∗ + r∗I I

∗ − rwDD∗ − ...

r∗MM∗ − aC(D∗, L∗, I∗)− FB

s.t. M +D + E + T = L+ I (parent’s resource constraint)

E

ωLL+ ωII≥ k (parent’s capital requirement)

M∗ +D∗ = L∗ + I∗ + T (branch’s resource constraint)

where T denotes the intrafirm transfer (T > 0 when the branch is lendingto the parent), and rwD denotes the interest rate on wholesale deposits.

Intra-temporal Problem: Matching Cross-Sectional Facts

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32

Two key assumptions needed:

1. FS > FB ;2. rwD > rD + fp.


Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32


1. FS > FB ;2. rwD > rD + fp.

Under these assumptions:

• fixed costs and monopolistic competition ⇒ foreign branches andsubsidiaries are larger (on average) than the incumbent firms;


Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32


1. FS > FB ;2. rwD > rD + fp.



• capital requirements ⇒ subsidiaries have lower borrowing needs(hence lower MC) than branches ⇒ subsidiaries are larger thanbranches both in terms of loans and investment;


Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32


1. FS > FB ;2. rwD > rD + fp.




• assumption 2 ⇒ branches have higher MC of deposits than subsidiaries⇒ subsidiaries are larger than branches in the deposits market;


Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32


1. FS > FB ;2. rwD > rD + fp.





• branches have higher MC than subsidiaries in all markets, but lowersunk costs (assumption 1), ⇒ selection of less efficient, smaller (moreefficient, larger) banks into branches (subsidiaries);


Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32


1. FS > FB ;2. rwD > rD + fp.






• the model can generate the observed differences in loans-to-assetratios between branches and subsidiaries (appropriately calibrating theelasticity of loans demand and the weights ωL, ωI );


Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

24 / 32


1. FS > FB ;2. rwD > rD + fp.






• the model can generate the observed differences in loans-to-assetratios between branches and subsidiaries (appropriately calibrating theelasticity of loans demand and the weights ωL, ωI );

• the model generates intrafirm transfers between parents and branches.

Inter-temporal Model: Aggregation and Shocks

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

25 / 32

Shocks to aggregate loans demand:

dL

L= µdt+ σdz

dL∗

L∗= µ∗dt+ σ∗dz∗

where µ, µ∗ ≥ 0, σ, σ∗ > 0 and dz, dz∗ are the increments of twostandard Wiener processes with correlation ρ ∈ [−1, 1].

Aggregation:

L =

(∫

L1−1/ηdL

)η/(η−1)

where η > 1 is also the elasticity of demand of each individual loan type.

[“Technical” role of these assumptions: ensure that interest rates on loans are

independent of L, and that each bank’s optimal profits in a country are a linear

affine function of L.]

Inter-temporal Model: Bellman Equations

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

26 / 32

Let Vi(a,L,L∗) denote the value of a bank with efficiency a with

international status i (i ∈ {N,B, S}), when aggregate loan demand in thetwo markets is described by (L,L∗):

Vi(a,L,L∗) = S(a,L) + Vi(a,L

∗)

where S(a,L) is the expected p.d.v. of domestic profits (independent of i),and Vi(a,L

∗) is the expected p.d.v. of foreign profits for a bank in status i.

Bellman equations:

S(a,L) = πN (a,L) + E[S(a,L′)|L]

VN (a,L∗) = max{

E[VN (a,L∗′)|a,L∗] ; VB(a,L∗)− FB ; ...

VS(a,L∗)− FS}

VB(a,L∗) = max

{

πB(a,L∗) + E[VB(a,L

∗′)|L∗] ; VN (a,L∗)}

VS(a,L∗) = max

{

πS(a,L∗) + E[VS(a,L

∗′)|L∗] ; VN (a,L∗)}

Inter-temporal Model: Value Functions

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

27 / 32

In the continuation regions:

S(a,L) =πN (a,L)

rM

VN (a,L∗) = AN (a)L∗α +BN (a)L∗β

VB(a,L∗) = AB(a)L

∗α +BB(a)L∗β +

πB

r∗M

VS(a,L∗) = AS(a)L

∗α +BS(a)L∗β +

πS

r∗M

where α and β are the roots of: 12σ

∗2ξ2 + (µ∗ − 12σ

∗2)ξ − rM = 0(α < 0, β > 1).

Value-matching and smooth pasting conditions deliver the parametersAi(a) and Bi(a) (i ∈ {N,B, S}) and the thresholds in aggregate loandemand that induce banks to enter or exit the foreign market (the policyfunction).

What does the Simulated Model Do?

Introduction

Data

Model

• Setup

• Why?

• Intra-temporal

• Inter-Temporal

Calibration

Results

Conclusions

28 / 32

• Parameterize the model and generate an economy with a large numberof domestic banks.1

• Simulate the stochastic process describing aggregate loan demand ineach country.

• Every period, the model delivers:

1. banks’ endogenous decisions of foreign entry (by type);2. banks’ endogenous decisions of exit from the foreign market (by

type);3. banks’ domestic and foreign flows of deposits, loans, investment,

interbank borrowing, and intrafirm transfers;4. banks’ domestic and foreign profits depending on the mode of

entry;5. banks’ domestic and foreign risk exposure (computed theoretically

as the covariance of a bank’s profits with domestic loan demand).

1Functional form assumptions: C(D,L, I) ≡ βLL+ βII2

2+ βDD2

2and management

efficiency x ≡ 1/a distributed according to G(x) = 1− bϑx−ϑ.

Calibration

Introduction

Data

Model

Calibration

• SMM

Results

Conclusions

29 / 32

Parameter Definition Value Source

Revenue and cost parametersp prob. of loan repayment 0.96 World Bankη elasticity of loan demandβL, βI , βD param. of cost functionk capital requirement (0.04, 0.08) Basel II/IIIωL, ωI weights for RWAfp insurance premium (0.005, 0.035) FDICFS , FB sunk entry costs

RatesrI av. return on investment 0.1 SP 500rD int. rate on retail deposits 0.0025 one-year CDrwD

int. rate on whol. deposits 0.006 LIBOR

Banks efficiency distributionϑ shape parameter .5η emp. loans size distrib.b location parameters 1 normalization

Brownian motionsµ, µ∗ drift 0 no growthσ, σ∗ st.dev. 0.06 st. dev. of loansρ correlation (-1,1) corr. of loansL0, L∗

0initialization

Calibration: SMM

Introduction

Data

Model

Calibration

• SMM

Results

Conclusions

30 / 32

• Direct calibration of p, k, fp, rI , rD, rwD, ϑ, b, µ, µ∗, σ, σ∗, ρ.

• Remaining 10 parameters: η, βL, βI , βD, ωL, ωI , FS , FB , L0, L∗

0

calibrated jointly to match moments from the data:

◦ average interest rates on loans (1 moment);◦ relative size of deposits and loans in branches compared to

subsidiaries (2 moments);◦ relative loans-to-assets ratios in branches compared to subsidiaries

(1 moment);◦ percentages of branches and subsidiaries in the total number of

banks in the US (2 moments);◦ entry and exit dynamics: average share of national banks that

become branches (subsidiaries) each year, and average share ofbranches (subsidiaries) exiting each year (4 moments).

Results and Extensions

Introduction

Data

Model

Calibration

Results

Conclusions

31 / 32

Use the model to evaluate the following counterfactual scenarios:

• changes in deposit insurance and capital requirements rules;• extending interbank transfers to subsidiaries;• elimination of the possibility of opening branches or subsidiaries.

Extensions:

• stochastic rates of return on investments;• shocks to deposits supply.

Conclusions

Introduction

Data

Model

Calibration

Results

Conclusions

32 / 32

• Growing interest (and literature!) on the operations of multinationalbanks.

• In this paper we provide a structural model that is designed toreproduce features of the foreign banking sector, including endogeneityof entry decisions and the choice of the mode of entry.

• The model has the potential to become a laboratory to conduct policyanalysis.

History of Banking Regulation

Introduction

Data

Model

Calibration

Results

Conclusions

Appendix

• Regulation

• Size differences

• Parameterization

33 / 32

• 1927 – McFadden Act prohibits interstate banking.

• 1978 – International Banking Act:

◦ brings foreign banks within the federal regulatory framework,◦ requires deposit insurance for branches of foreign banks engaged in

retail deposit taking in the U.S.

• 1991 – FBSEA (Foreign Bank Supervision Enhancement Act), part ofFDICIA (Federal Deposit Insurance Corporation Improvement Act):

◦ eliminates deposit insurance for branches of foreign banks.

• 1994 – Riegle-Neal Interstate Banking and Branching Efficiency Act:

◦ adequately capitalized and managed Bank Holding Companies(BHCs) are permitted to acquire banks in any state. The law is thesame for both domestic and international banks.

Size Distributions

Introduction

Data

Model

Calibration

Results

Conclusions

Appendix

• Regulation



34 / 32

0.2

.4.6

.81

F

0 5 10 15 20Log of Total Deposits

Foreign−Subsidiaries Foreign−Branches

Source: only foreign−owned institutions

Date: Q4/2010Cumulative Size Distribution − Deposits

0.2

.4.6

.81

F

5 10 15 20Log of Total Loans



Date: Q4/2010Cumulative Size Distribution − Loans

0.2

.4.6

.81

F

0 5 10 15 20Log of Total Assets



Date: Q4/2010Cumulative Size Distribution − Assets

On Modeling Deposit Insurance

Introduction

Data

Model

Calibration

Results

Conclusions

Appendix

• Regulation



35 / 32

The FDIC determines the deposit insurance premium (or “assessment”) ona risk basis. A bank’s assessment is calculated by multiplying itsassessment rate AR by its assessment base, where a bank’s assessmentbase is equal to its average consolidated total assets minus its averagetangible equity.2

Hence the total premium Fp is given by:

F p = AR · (L+ I − 1M<0M − E) ≈ fp ·D

where the parameter fp is given by the assessment rate:

I II III IV TotalAssessment Rate (pct. points) 5 to 9 14 23 35 5 to 35

2Definition from the Dodd-Frank Act.

On the Loan Size Distribution

Introduction

Data

Model

Calibration

Results

Conclusions

Appendix

• Regulation



36 / 32

Regress log rank on log size ⇒ ϑ/η = .5.

multinational banks - boston universitypeople.bu.edu/garettos/fgg_nyfed_12_13_new.pdf ·...

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