capital flows global liquidity slides dec 02 2012bsorense/capital_flows_global_liquidity... ·...

Capital Flows, Cross-Border Banking and

Global Liquidity

Valentina Bruno Hyun Song Shin

December 2012

Bruno and Shin: Capital Flows, Cross-Border Banking and Global Liquidity 1

Main Themes

• Global financial conditions closely intertwined with cross-border banking

• Global factors drive capital flows into diverse destination countries

• US dollar as currency underpinning cross-border banking

• European banks as intermediaries in cross-border banking


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Tril

lion

dolla

rsNet interoffice assets Large time deposits Borrowings from banks in U.S.Borrowings from others Securities Loans and leasesCash assets

Figure 1. Assets and liabilities of foreign banks in the U.S. (Source: Federal Reserve H8 weekly series onassets and liabilities of foreign-related institutions)


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Net interoffice assets of foreign banks in US

Figure 2. Net interoffice assets of foreign banks in U.S. given by negative of Federal Reserve weekly H8 serieson “net due to related foreign offices of foreign-related institutions”


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(%)

Asia

United States

Other Europe

Other euro area

Belgium, Italy,

Spain, Portugal,

Ireland, Greece

Figure 3. Amount owed by banks to US prime money market funds (% of total), based on top 10 primeMMFs, representing $755 bn of $1.66 trn total prime MMF assets (Source: IMF GFSR Sept 2011, datafrom Fitch).


Impact on US Financial Conditions

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19911992199319941995199619971998199920002001200220032004200520062007200820092010

Tri

llio

n D

oll

ars Liabilities: Foreign official

assets in United States (line 56)

Liabilities: Foreign claims onU.S. non-banks (line 68)

Liabilities: Foreign claims onU.S. banks and securitiesbrokers (line 69)

Liabilities: Foreign privateholding of U.S. securities otherthan Treasurys (line 66)

Assets: US holding of foreignsecurities (line 52)

Assets: Claims of U.S. non-banks on foreigners (line 53)

Assets: Claims of U.S. banksand securities brokers onforeigners (line 54)

Figure 4. US gross capital flows by category (Source: US Bureau of Economic Analysis). Increase in USliability to foreigners is indicated by positive bar, increase in US claims on foreigners is indicated by negativebar.


US Households

USBorrowers

US Banking Sector

EuropeanGlobal Banks

border

Wholesalefunding market

Shadow bankingsystem

Figure 5. European global banks add intermediation capacity for connecting US savers and borrowers


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ars

Non-European BISreporting countries

Other European BISreporting countries

Switzerland

United Kingdom

France

Germany

Figure 6. Foreign claims of BIS reporting banks on US counterparties (Source: BIS consolidated bankingstatistics, Table 9D)


Borrowersin A

Borrowersin B

Borrowersin C

Banksin A

Banksin B

Banksin C

GlobalBanks

WholesaleFundingMarket

Figure 7. Topography of global liquidity


Borrowersin A

Borrowersin B

Borrowersin C

Banksin A

Banksin B

Banksin C

GlobalBanks

WholesaleFundingMarket

Figure 8. Topography of global liquidity


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Mar 2003 =100

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Ireland

Spain

Turkey

Australia

South Korea

Chile

Brazil

South Africa

Figure 9. Cross-border claims (loans and deposits) of BIS reporting banks on counterparties listed on right(Source: BIS locational banking statistics Table 7A)


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Latvia

Lithuania

Estonia

Iceland



Outline

• “Double-decker” model of credit supply

— Global banks and local banks— Capital flows through banking sector

• Bank credit supply

— Leverage tied to risk measures (VIX, VaR)— Deviation from standard portfolio rules

• Empirical hypotheses and investigation


Corporate Finance of Banking

A L

Assets

Equity

Debt


A L

AssetsEquity

Debt

A L

Assets

Equity

Debt


A L

Assets

Equity

Debt

A L

Assets

Equity

Debt


Barclays: 2 year change in assets, equity, debt and risk-weighted assets (1992 -2010)

y = 0.9974x - 0.175R2 = 0.9998

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ar c

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ity, d

ebt a

nd

risk-

wei

ghte

d as

sets

(bi

llion

pou

nds)

2yr RWAChange

2yr EquityChange

2yr DebtChange

Figure 11. Barclays: 2 year change in assets, equity and debt (1992-2010) (Source: Bankscope)


Turning Credit Risk Model on Its Head

• Vasicek one factor credit risk model (backbone of Basel)

• Turn Vasicek model on its head as credit supply model

— Fix E. Determine credit supply S

S =E

1− 1+r1+fϕ (ρ, α, ε)

, ϕ ∈ (0, 1)

ϕ is ratio of notional debt to notional assets

[ϕ is normalized leverage measure, with ϕ ∈ (0, 1)]


Credit Supply

Notation for balance sheet of bank

C

E

f+1r+1L

Bank


Vasicek (2002) extension of Merton (1974) to allow for many borrowers j

tT0

F

( )0V

default probability

Project value

0

Figure 12. Value of projects of local borrowers and default probability


VT = V0 exp

��µ− s2

2

�T + s

√TWj

�

Prob (VT < F ) = Φ

Wj < −

ln (V0/F ) +�µ− s2

2

T

s√T

= Φ(−dj)

Vasicek (2002):

Wj =√ρY +

1− ρXj


Borrower j repays the loan when Zj ≥ 0, where Zj is the random variable:

Zj = dj +√ρY +

1− ρXj

= −Φ−1 (ε) +√ρY + 1− ρXj

Realized value of assets at date 1

w (Y ) ≡ (1 + r)C · Pr (Zj ≥ 0|Y )

= (1 + r)C · Pr�√

ρY + 1− ρXj ≥ Φ−1 (ε) |Y

= (1 + r)C · Φ�Y√ρ−Φ−1(ε)√1−ρ


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ets

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ets

ε = 0.2

ε = 0.3

ρ = 0.3 ε = 0.2

ε = 0.1

ρ = 0.01

ρ = 0.1

ρ = 0.3

Figure 13. The two charts plot the densities over realized assets when C (1 + r) = 1. The left handcharts plots the density over asset realizations of the bank when ρ = 0.1 and ε is varied from 0.1 to 0.3.The right hand chart plots the asset realization density when ε = 0.2 and ρ varies from 0.01 to 0.3.


c.d.f. of w

F (z) = Pr (w ≤ z)

= Pr�Y ≤ w−1 (z)

�

= Φ�w−1 (z)

�

= Φ

Φ−1 (ε) +

√1− ρΦ−1

�z

(1+r)C

√ρ


Bank Behavior

Value-at-Risk (VaR) rule with insolvency probability to α > 0 when notionalliability is (1 + f)L.

Private credit C determined from

Pr (w < (1 + f)L) = Φ

�Φ−1(ε)+

√1−ρΦ−1

�(1+f)L(1+r)C

�

√ρ

�

= α

Notional liabilities

Notional assets=(1 + f)L

(1 + r)C= Φ

�√ρΦ−1 (α)−Φ−1 (ε)√

1− ρ

�(1)

where

ϕ (α, ε, ρ) ≡ Φ�√

ρΦ−1(α)−Φ−1(ε)√1−ρ


Supply of Credit

Credit supply C and demand for funding L is obtained from (1) and balancesheet identity C = E + L

C =E

1− 1+r1+f · ϕ

, L =E

1+f1+r · 1ϕ − 1

Aggregation holds due to proportionality

Leverage =1

1− 1+r1+f · ϕ

Risk premium is well-defined

Risk premium = (1− ε) (1 + r)− 1


Double-decker model of Global Liquidity

C

RE

M

GE

i+1f+1r+1L

L

Regional Bank Global Bank

Figure 14. Regional and global bank balance sheets


Diversified loan portfolio from region k

Regions

Borrowers

Regional bank in k

k

jBorrower jin region k

Diversified loan portfolioacross regional banks

Global bank

Figure 15. Global and regional banks


Global, Regional and Idiosyncratic Risk Factors

Zkj ≡ −Φ−1 (ε) +√ρYk +

1− ρXkj

Yk =

βG+ 1− βRk

Regional bank k defaults when

Yk < w−1 ((1 + f)L) = 1√ρ

�Φ−1 (ε) +

1− ρΦ−1 (ϕ)

Or when ξk < 0

ξk ≡ √ρYk −Φ−1 (ε)−

1− ρΦ−1 (ϕ)

=

ρβG+

ρ (1− β)Rk −Φ−1 (ε)− 1− ρΦ−1 (ϕ)


Asset realization is deterministic function of global risk factor G

w (G) = (1 + f)L · Pr (ξk ≥ 0|G)

= (1 + f)L · Pr�Rk ≥ Φ−1(ε)+

√1−ρΦ−1(ϕ)√

ρ(1−β)−�

β1−βG

��G�

= (1 + f)L · Φ��

β1−βG−

Φ−1(ε)+√1−ρΦ−1(ϕ)√

ρ(1−β)

�

Quantiles follow from the c.d.f. of w (G).

F (z) = Pr (w (G) ≤ z)

= Pr�G ≤ w−1 (z)

�

= Φ�w−1 (z)

�


where

w−1 (z) =�

1−ββ

�Φ−1

�z

(1+f)L

+ Φ−1(ε)+

√1−ρΦ−1(ϕ)√

ρ(1−β)

�

Global bank Value-at-Risk (VaR) rule with insolvency probability γ > 0.Notional liability of the global bank is (1 + i)M .

γ = Pr (w (G) < (1 + i)M)

= Φ

��1−ββ

�Φ−1

�(1+i)M(1+f)L

+ Φ−1(ε)+

√1−ρΦ−1(ϕ)√

ρ(1−β)

��


Notional liabilities

Notional assets=

(1 + i)M

(1 + f)L

= Φ

�√ρβΦ−1(γ)−Φ−1(ε)−

√1−ρΦ−1(ϕ)√

ρ(1−β)

�

≡ ψ (γ, α, β, ε, ρ)

Cross-border loan supply

L =EG

1− 1+f1+iψ


0 L

( )αα −1/

11 −+ψ

i

( )fLS

( )fLD

f

( ) 11 −+ rϕ

( )( )i

EG

+−−

111

αψ

Figure 16. Equilibrium cross-border lending L


Capital Flows and Domestic Credit

Market clearing for L

ER1+f1+r · 1ϕ − 1

=EG

1− 1+f1+iψ

Private credit

C =EG +ER

1− 1+r1+iϕψ

Total privatecredit

=Aggregate bank capital (regional + global)

1− spread× regionalleverage

× globalleverage


Risk premium in recipient economy

π ≡ (1− ε) (1 + r)− 1

Equilibrium stock of cross-border lending L

L =EG + ER · 1+r1+iϕψ

1− 1+r1+iϕψ

Total cross-border lending

=Global and weighted regional bank capital

1− spread× regionalleverage

× globalleverage


Comparative Statics

Global factors EG and ψ

∆L ≃ ∂L

∂EG∆EG +

∂L

∂ψ∆ψ

=1

1− ϕψ∆EG +

�(1− ϕψ)ERϕ− (EG +ERϕψ) (−ϕ)

(1− ϕψ)2

�

∆ψ

=1

1− ϕψ∆EG + C

ϕ

1− ϕψ∆ψ

Banking sector capital flows (i) increase with ∆EG (ii) increase with bankleverage (iii) increase in change in bank leverage


Impact of Currency Appreciation

Local borrower Local bank

A A LL

r+1Global bank

Localcurrency

US dollars

US dollars

US dollars

f+1

border

Figure 17. Local non-bank borrowers have currency mismatch


Impact of Currency Appreciation

tT0

F

default probability

Project value

0

Effect of currency appreciation

Figure 18. Currency appreciation lowers probability of default ε


Empirical Counterparts

Leverage ( =(total liabilities + equity)/equity)

2009Q1

2007Q2

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Figure 19. Leverage of US Securities broker dealer sector (Source: Federal Reserve Flow of Funds)


Leverage and VIX

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log_vix(-1)

BD

le

ve

rag

e

Figure 20. Scatter chart of broker-dealer leverage and lagged log VIX


Table 1. Broker dealer leverage and VIX. This table presents OLS regressions with broker dealer leverageas the dependent variable and the one-quarter lagged log VIX index as the explanatory variable. Column 2includes the post-crisis dummy that takes the value 1 after 2007Q4 and zero otherwise.

1 2

VIX(-1) -0.058*** -0.031***

[0.000] [0.008]

Post-crisis dummy -0.059***

[0.000]

Constant 0.379*** 0.312***

[0.000] [0.000]

Observations 64 64

R2

0.20 0.471

Adjusted R2

0.187 0.453


Two Sets of Panel Regressions

Set 1: US broker dealer leverage as proxy for global banks’ leverage

∆Lc,t = β0 + β1 ·∆Interofficet + β2BD Leveraget−1 + β3 ·∆BD Leveraget

+β4∆Equityc,t + β5∆RERt−1 + controlsc,t + ec,t

Set 2: VIX as proxy for global banks’ leverage

(include residual of BD leverage regression on VIX as a check)

In both cases, gauge relative impact of global versus local variables


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Mar 2003 =100

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Jun.2007

Mar.2008

Dec.2008

Sep.2009

Jun.2010

Mar.2011

Dec.2011

Ireland

Spain

Turkey

Australia

South Korea

Chile

Brazil

South Africa



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Figure 22. This figure plots cross-border banking sector capital flows as year-on-year growth in externalclaims of BIS-reporting banks (Table 7A). The VIX series is the quarterly average of CBOE VIX index.


Sample

Sample of 46 countries with largest foreign bank penetration (Claessens,van Horen, Gurcanlar and Mercado (2008))

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile,Cyprus, Czech Republic, Denmark, Egypt, Estonia, Finland, France,Germany, Greece, Hungary, Iceland, Indonesia, Ireland, Israel, Italy, Japan,Latvia, Lithuania, Malaysia, Malta, Mexico, Netherlands, Norway, Poland,Portugal, Romania, Russia, Slovakia, Slovenia, South Korea, Spain, Sweden,Switzerland, Thailand, Turkey, Ukraine, United Kingdom and Uruguay.


Summary StatisticsVariable Frequency Obs Mean Std. Dev. Min Max

Dependent Variable

∆Loan Quarter 2944 0.025 0.090 -0.172 0.240

Global Variables

∆Interoffice Quarter 64 0.087 0.515 -1.362 1.908

VIX Quarter 64 3.045 0.347 2.433 3.787

BD Leverage Quarter 64 0.203 0.046 0.124 0.304

∆Equity Annual 14 0.131 0.219 -0.266 0.697

Local Variables

∆RER Quarter 2942 -0.002 0.068 -0.510 1.030

∆M2 Annual 532 0.135 0.152 -0.253 1.413

GDP growth Annual 532 0.080 0.078 -0.208 0.607

Debt to GDP Annual 532 0.517 0.284 0.067 1.272

Inflation Annual 532 0.046 0.054 -0.004 0.365

Stock volatility Annual 465 3.213 0.425 2.195 4.705

Bank ROA Annual 465 0.007 0.011 -0.041 0.026


1 2 3 4 5 6

∆Interoffice(-1) 0.0179*** 0.0048 0.0065**[0.000] [0.137] [0.048]

BD Leverage(-1) 0.5485*** 0.5487*** 0.4091***[0.000] [0.000] [0.000]

∆BD Leverage 0.2067*** 0.1900*** 0.1793***[0.000] [0.000] [0.000]

∆Equity 0.0301*** 0.0272***[0.002] [0.004]

∆RER(-1) -0.1452*** -0.1502*** -0.0892***[0.000] [0.000] [0.005]

∆M2(-4) 0.0586** 0.0421**[0.021] [0.036]

GDP growth(-4) 0.1122* 0.0576[0.093] [0.290]

DEBT/GDP(-4) -0.0066 -0.0286[0.718] [0.173]

Inflation(-4) -0.2278** -0.1755*[0.044] [0.069]

Stock volatility(-4) -0.0295*** 0.0049[0.000] [0.588]

Bank ROA(-4) 1.5050*** 1.4313***[0.000] [0.000]

Constant 0.0237*** -0.0855*** -0.0905*** 0.0251*** 0.1082*** -0.0734*[0.000] [0.000] [0.000] [0.000] [0.000] [0.060]

Observations 2,944 2,944 2,576 2,942 2,020 2,020

R2

0.011 0.097 0.112 0.013 0.113 0.176# countries 46 46 46 46 44 44


1 2 3 4 5 6

∆Interoffice(-1) 0.0126*** 0.0140*** 0.0113*** 0.0120*** 0.0097*** 0.0109***[0.001] [0.000] [0.002] [0.000] [0.006] [0.002]

VIX(-1) -0.0719*** -0.0533*** -0.0491*** -0.0438*** -0.0501*** -0.0455***[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

∆VIX -0.0303*** -0.0214*** -0.0270*** -0.0272*** -0.0300*** -0.0297***[0.000] [0.002] [0.000] [0.001] [0.000] [0.001]

∆Equity -0.0272*** 0.3492** 0.2155* 0.1304 0.2023* 0.1285[0.002] [0.013] [0.068] [0.204] [0.082] [0.210]

∆Equity∗VIX(-1) -0.1224*** -0.0755** -0.0471 -0.0697* -0.0456[0.007] [0.046] [0.152] [0.059] [0.162]

Leverage Residual 0.1490** 0.1041[0.040] [0.218]

∆RER(-1) -0.1264*** -0.1156*** -0.1191*** -0.1098***[0.000] [0.000] [0.000] [0.001]

∆M2(-4) 0.0602*** 0.0485** 0.0585*** 0.0475**[0.007] [0.021] [0.007] [0.021]

GDP growth(-4) 0.2628*** 0.1313** 0.2423*** 0.1272**[0.000] [0.042] [0.000] [0.046]

DEBT/GDP(-4) -0.0761*** -0.0370* -0.0685*** -0.0353*[0.000] [0.064] [0.001] [0.081]

Inflation(-4) -0.3526*** -0.1964* -0.3361*** -0.1944*[0.000] [0.067] [0.000] [0.064]

Stock Volatility(-4) -0.0120* -0.0076[0.091] [0.295]

Bank ROA(-4) 1.2705*** 1.2720***[0.000] [0.000]

Constant 0.2460*** 0.1856*** 0.2004*** 0.1995*** 0.1996*** 0.1890***[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

Observations 2,576 2,576 2,300 2,020 2,300 2,020

R2

0.071 0.076 0.137 0.153 0.139 0.154# Countries 46 46 46 44 46 44


Individual Country Effects

Separate panel regressions for each country:

∆Lc,t = βc,0 + βc,1VIXt−1 + βc,2VIXt−1 ∗ Countryc

+βc,3∆Interofficet + controlsc,t + ec,t

∆Lc,t = βc,0 + βc,1∆Interofficet + βc,2∆Interofficet ∗ Countryc

+βc,3VIXt−1 + controlsc,t + ec,t

Sum βc,1 + βc,2 measures the total effect on country c. βc,2 measuresincremental country-specific effect.


1 2 3 4 5 6

∆Interoffice 0.0104*** 0.0076*** 0.0074*** 0.0074*** 0.0075*** 0.0076***[0.000] [0.002] [0.003] [0.003] [0.002] [0.002]

∆Interoffice*Korea 0.0107*** 0.0195***[0.000] [0.000]

∆Interoffice*Korea*Post 2010 -0.0314***[0.000]

VIX -0.0629*** -0.0498*** -0.0498*** -0.0499*** -0.0485*** -0.0485***[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

VIX *Korea -0.0621*** -0.0631***[0.000] [0.000]

VIX *Korea*Post 2010 0.0026*[0.071]

∆VIX -0.0214*** -0.0211*** -0.0212*** -0.0211*** -0.0212*** -0.0212***[0.001] [0.001] [0.001] [0.001] [0.001] [0.001]

RER -0.0481*** -0.0549*** -0.0547*** -0.0547*** -0.0539*** -0.0539***[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

∆Money stock 0.7617*** 0.7618*** 0.7620*** 0.7628*** 0.7627***[0.000] [0.000] [0.000] [0.000] [0.000]

GDP Growth 0.3008*** 0.3002*** 0.3001*** 0.3013*** 0.3012***[0.000] [0.000] [0.000] [0.000] [0.000]

Debt to GDP -0.0806** -0.0805** -0.0806** -0.0813** -0.0814**[0.015] [0.015] [0.015] [0.014] [0.014]

Constant 0.2962*** 0.2729*** 0.2728*** 0.2731*** 0.2720*** 0.2720***[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

Observations 3,120 2,892 2,892 2,892 2,892 2,892R-squared 0.057 0.146 0.146 0.146 0.147 0.147Number of countries 48 48 48 48 48 48


βc,1+βc,2≥ 0 βc,1+βc,2≤ 0βc,2 VIX*Estonia -0.0033 Reject ∆Interoffice*Estonia 0.0189*** Reject

[0.690] [0.000]

βc,2 VIX*Latvia -0.0235** Reject ∆Interoffice*Latvia 0.0063** Reject

[0.027] [0.017]

βc,2 VIX*Turkey -0.0052 Reject ∆Interoffice*Turkey -0.0031 Reject

[0.533] [0.350]

βc,2 VIX*Brazil -0.0033 Reject ∆Interoffice*Brazil -0.0064** Reject

[0.511] [0.014]

βc,2 VIX*Chile 0.0811*** Do not Reject ∆Interoffice*Chile -0.0121*** Do not Reject

[0.000] [0.000]

βc,2 VIX*Spain 0.0378*** Do not Reject ∆Interoffice*Spain 0.0147*** Reject

[0.000] [0.000]

βc,2 VIX*UK 0.0051 Reject ∆Interoffice*UK -0.0156*** Do not Reject

[0.395] [0.000]

βc,2 VIX*Germany 0.0162*** Reject ∆Interoffice*Germany -0.0021 Reject

[0.002] [0.384]

βc,2 VIX*Japan 0.0706*** Do not Reject ∆Interoffice*Japan -0.0327*** Do not Reject

[0.000] [0.000]


Table 2. Testing for endogeneity. Arellano and Bond (1991) dynamic system GMM. The tests indicatethat the residuals in first differences (AR(1)) are correlated, but there is no serial correlation in seconddifferences (AR(2)).

1 2

∆Interoffice(-1) 0.0306*** 0.0297***[0.000] [0.000]

BD Leverage(-1) 0.6244***[0.000]

VIX(-1) -0.0519***[0.001]

∆BD Leverage 0.0064[0.901]

∆VIX 0.0025[0.888]

∆Equity 0.0525** 0.0632***[0.044] [0.009]

∆L(-1) 0.2134 -0.068[0.323] [0.709]

Constant -0.1297*** 0.2435***[0.005] [0.001]

Country controls Y YObservations 2,300 2,300# countries 46 46

AR(1) p-value 0.004 0.015AR(2) p-value 0.25 0.74Hansen test p-value 0.132 0.239


Table 3. Accounting for global factors. This table compares the adjusted R-squared statistics obtainedfrom OLS regressions with time dummies, global variables (∆Interoffice, Leverage, ∆Leverage and∆Equity),and local variables (GDP growth, Debt/GDP, Inflation, and ∆M2). Panel A is for the full sample ofcountries. Panels B is for the sample with large foreign bank presence and Panel C is for low foreign bankpresence.

1 2 3 4 5

Panel A: All sample

Time dummies Y YGlobal variables Y YLocal variables Y Y Y

Adjusted R-squared 0.221 0.115 0.0852 0.154 0.269Observations 2,300 2,300 2,300 2,300 2,300

Panel B: High foreign bank presence


Adjusted R-squared 0.3 0.165 0.136 0.205 0.36Observations 952 952 952 952 952

Panel C: Low foreign bank presence


Adjusted R-squared 0.193 0.101 0.0554 0.136 0.231Observations 1,348 1,348 1,348 1,348 1,348


Robustness Analysis

• Crisis period dummy

— Effect is larger during crisis period— But also at work during non-crisis periods

• Developing country dummy

— No difference between developing and developed economies— Europe effect?


Two Themes

• Global financial conditions closely intertwined with cross-border banking

• Global factors drive capital flows into diverse destination countries

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