Channels of Crisis Transmission in the Global Banking Network Galina - - PowerPoint PPT Presentation

Channels of Crisis Transmission in the Global Banking Network

*The views expressed herein are those of the authors and should not be attributed to the Federal Reserve System, Fannie Mae, the IMF, their Executive Boards, or their management.

Galina Hale (FRBSF) Tümer Kapan (Fannie Mae) Camelia Minoiu (IMF)

The Story in a Nutshell

In 2010, Citibank NA had syndicated loan exposures vis-à-vis 105 banks in 94 countries.

__ OECD __ non-OECD

Motivation

• Complexity of financial linkages has increased

in recent times and may have contributed to the severity and reach of the global financial crisis

• Complexity may play a role in how systemic

banking crises are transmitted internationally

– Ongoing efforts on banking regulation – Debate on what bank-specific “systemicness” is

Question

• Study the role of financial systemic complexity

in the transmission of shocks worldwide:

– Direct channels – Indirect channels

• Study the impact of financial linkages on bank

performance / profitability

– Bank profitability matters because it predicts survival

Aim

• Disentangle the direct vs. indirect channels

through which crises are transmitted globally:

– Direct exposures

• First degree connections

– Indirect exposures

• Higher degree connections

– Relative position in the network

• Centrality in the network

Contribution

• First paper to use bank-level lending data to
• 1. Construct global banking networks (GBN)
• 2. Compute bank-level measures of

interconnectedness and

• 3. Directly relate interconnectedness to bank

performance (2,000 interconnected banks are linked to their financials during 1997-2010)

Hypotheses

• Theory: interconnectedness carries both

– Benefits: diversification, shock diffusion and – Risks: facilitates transmission of shocks/contagion

• Banking linkages may play a different role during

normal and crisis times

– Normal times: portfolio diversification concerns, search for yield, advantageous market position – Crisis times: direct losses and contagion, but ability to spread losses through linkages and to leverage past connections to obtain funding during crunches

Formally

• Bank performance Y is affected by crises in its

country C and performance of banks it is exposed to (directly or indirectly)

• Substituting for Yj

direct exposure indirect exposure network distance decay factor

… adding network measures

Expanding:

network characteristics small and insignificant

Data Construction

• Use data on bank-to-bank lending from the

international syndicated market for 1990-2010 from Dealogic’s Loan Analytics

– Carefully clean up bank names, adjust for bank name changes, merges and acquisitions, etc. – Split total loan volume by bank (pro-rata) – Construct 2 GBNs

• Merge with bank balance sheet data from

Bankscope

• Systemic banking crisis dates: Laeven and Valencia

(2012)

Example: Syndicated loan to a British investment bank

11

Borrower: Investec Bank (UK) Ltd. Industry: Private sector bank Signing date: March 28, 2006 Deal type: Investment grade Maturity: 3 years Amount: GBP 445 million Interest rate: LIBOR + 120bps Participating banks (15): BayernLB; Bank of Montreal (London); Bank of Tokyo-Mitsubishi UFJ Ltd; Commerzbank International Luxembourg SA; Dresdner Kleinwort Wasserstein; HSH Nordbank AG (London); ING Bank NV; KBC; Lloyds TSB Bank plc; Mizuho Corporate Bank Ltd; Royal Bank of Scotland plc; SG Corporate & Investment Banking; Standard Chartered Bank; Sumitomo Mitsui Banking Corp Europe Ltd; Wachovia Bank NA Nationalities (7): Germany, UK, Japan, Luxembourg, Netherlands, Belgium, France Source: Loan Analytics

Exposure through syndicated lending to banks is about 37 percent of BIS loans to banks

5 10 15 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Exposures to banks in all countries

Syndicated loans BIS claims

Global Banking Networks (GBN)

• 1. Network of current exposures EGBN

– Based on bank-to-bank loan borrowing/lending exposures in the syndicated market:

• Computed for every year t using information on outstanding

loans (based on loan maturities)

• 2. Network of current and past relationships RGBN

– Based on all current and past borrowing/lending relationships in the syndicated market:

• Reflects entire history of financial transactions
• Captures information flows and learning (Hale, 2012)
• May capture linkages in other lines of business

Both networks are directed; use only asset-based connectivity

EGBN 2007 – subnetwork of 100 largest banks by 2007 assets

Bank-to-bank Lending and Connections

50 100 150 200 250 300 350 400 450 500 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 55,000 60,000 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

total lending volume, current USD bn (right-axis) # current relationships (EGBN) # past relationships (RGBN)

Bank-level Measures of Interconnectedness

• Two concepts:

– Distance: # of banks a bank has to go through to reach another bank (in the shortest possible way) – Proximity: 1/distance

Bank-level Measures of Interconnectedness

1. Direct exposures

– In USD or # of direct financial partners (distance=proximity=1)

2. Indirect exposure

– Proximity to the banks from each country excluding directly linked banks (0<proximity<1)

3. Relative position in the network

– Betweenness Centrality (“key intermediary”) Indicator for banks that link different groups of banks in the network – Closeness Centrality Average proximity to all the other banks in the network – Proximity to Network Center Proximity to the network’s most centrally-located bank

Which Are the “Network Centers”?

These banks have the highest closeness centrality in the binary and weighted EGBN and RGBN.

Year Bank Country Bank Country 1997 JP Morgan US JP Morgan US 1998 JP Morgan US LRP Germany 1999 BayernLB Germany WestLB Germany 2000 BayernLB Germany Citibank US 2001 BayernLB Germany Unicredit Germany 2002 BayernLB Germany HSBC UK 2003 WestLB Germany HSBC UK 2004 HSBC UK HSBC UK 2005 HSBC UK HSBC UK 2006 Santander Spain Santander Spain 2007 BBVA Spain BBVA Spain 2008 BBVA Spain BBVA Spain 2009 BBVA Spain BBVA Spain 2010 BBVA Spain BBVA Spain

EGBN RGBN

What is a “Key Intermediary”?

In 2010, Arab Bank PLC (Jordan) had syndicated loan claims on 16 banks and liabilities vis-à-vis 29 banks .

__ lenders __ borrowers

Empirical Approach

Regression Set-Up

• Panel regressions:

– Dataset: 2,000 banks from 67 countries over 1997-2010 – Dependent variable: ROA, ROE

• Controls:

– Bank size (log-assets) – Capital (equity/assets) – Indicator for crisis in bank’s home country – Type of entity dummies – Specialization dummies – Bank country FE – Year FE

• St. errors clustered on bank

Main Covariates

• Direct and indirect

exposures:

– Computed vis-à-vis crisis and non-crisis countries

• $ exposures (EGBN)
• # current exposures (EGBN)
• # past exposures (RGBN)
• Network position

– Closeness, betweenness, and proximity to network’s center – Interacted with crisis in the bank’s home country

Effect of Direct and Indirect Exposures

• n Bank Performance – Baseline

(1) (2) (3) (4)

• L. Direct US$ current exposure to non-crisis countries

0.003 (0.019)

• L. Direct US$ current exposure to crisis countries
• 0.104***

(0.037)

• L. Direct current exposure to non-crisis countries
• 0.000

0.000 0.003* (0.001) (0.002) (0.001)

• L. Direct current exposure to crisis countries
• 0.012*** -0.009**
• 0.009*

(0.004) (0.004) (0.005)

• L. Direct past exposure to non-crisis countries
• 0.001
• 0.001

(0.001) (0.001)

• L. Direct past exposure to crisis countries
• 0.009**
• 0.009**

(0.003) (0.003)

• L. Indirect proximity to non-crisis countries
• 0.028**

(0.011)

• L. Indirect proximity to crisis countries

0.033 (0.038) Observations 9,129 9,129 9,129 9,129 R-squared 0.333 0.334 0.334 0.335

Effect of Direct and Indirect Exposures

• n Bank Performance – Robustness

(1) (2) (3) (4) (5) (6) Benchmark ROE Drop 1% Bank FE Average for indirect Cluster on country

• L. Direct current exposure to non-crisis countries 0.003*

0.008 0.003* 0.001 0.001 0.003* (0.001) (0.022) (0.001) (0.002) (0.001) (0.002)

• L. Direct current exposure to crisis countries
• 0.009*
• 0.112*
• 0.008*
• 0.002
• 0.008*
• 0.009*

(0.005) (0.061) (0.004) (0.005) (0.004) (0.005)

• L. Direct past exposure to non-crisis countries
• 0.001

0.008

• 0.001

0.002

• 0.001
• 0.001

(0.001) (0.014) (0.001) (0.002) (0.001) (0.002)

• L. Direct past exposure to crisis countries
• 0.009**
• 0.071
• 0.010***
• 0.009*
• 0.009***
• 0.009**

(0.003) (0.044) (0.003) (0.005) (0.003) (0.004)

• L. Indirect proximity to non-crisis countries
• 0.028**
• 0.195
• 0.028**
• 0.029**
• 11.249*
• 0.028**

(0.011) (0.132) (0.011) (0.014) (6.005) (0.011)

• L. Indirect proximity to crisis countries

0.033 0.768 0.035 0.046 12.519 0.033 (0.038) (0.496) (0.040) (0.047) (16.188) (0.049) Observations 9,129 9,128 8,239 9,129 8,322 9,129 R-squared 0.335 0.203 0.328 0.557 0.330 0.335

Effect of Relative Position in the Network

• n Bank Performance - Baseline

(1) (2) (3) (4)

• L. Key intermediary

0.057 0.149* (0.074) (0.077)

• L. Key intermediary * Crisis in home country
• 0.286*
• 0.538***

(0.173) (0.185)

• L. Closeness centrality
• 0.848**
• 1.920***

(0.391) (0.649)

• L. Closeness centrality * Crisis in home country

1.439* 6.014*** (0.783) (1.352)

• L. Closeness to network center

0.009 0.142** (0.039) (0.065)

• L. Closeness to network center * Crisis in home

country

• 0.227** -0.627***

(0.098) (0.155) Benchmark regressors included? yes yes yes yes Observations 8,342 8,342 8,342 8,342 R-squared 0.331 0.331 0.331 0.335

Conclusions: The “dark side” of being connected

• Direct current & past exposures to banks in

crisis countries reduce profitability (effect is small)

• Banks that are centrally-located in the

network -- “key intermediaries” and banks that are close to the network’s center -- have lower profitability during crises in their home countries

Conclusions: The “bright side” of being connected

• There are rents associated with being a “key

intermediary” or close to the network’s center during tranquil times

• There are also benefits associated with being

the network center during crises in its home country

• Work in progress:

– Results on indirect proximities – Mechanisms behind our results (e.g., defaults)

Other slides

Correlation matrix

Return on assets Equity/As sets Log (assets) Crisis in bank's home country Direct current exposure to non- crisis countries (US$) Direct current exposure to crisis countries (US$) Direct current exposure to non- crisis countries Equity/Assets 0.3429* Log(assets)

• 0.0976*
• 0.3183*

Crisis in bank's home country

• 0.2182*
• 0.0342*

0.0988* Direct current exposure to non-crisis countries (US$)

• 0.0107
• 0.0834*

0.3947* 0.0546* Direct current exposure to crisis countries (US$)

• 0.0171
• 0.0387*

0.1663*

• 0.0506*

0.2107* Direct current exposure to non-crisis countries

• 0.0249*
• 0.0977*

0.3578* 0.0336* 0.8055* 0.2042* Direct current exposure to crisis countries

• 0.0380*
• 0.0398*

0.1446*

• 0.0914*

0.2056* 0.6279* 0.2554* Direct past exposure to non-crisis countries

• 0.0287*
• 0.0696*

0.3748* 0.1356* 0.5737* 0.0895* 0.6175* Direct past exposure to crisis countries

• 0.0101
• 0.0263*

0.1711*

• 0.0943*

0.1223* 0.3841* 0.1475* Indirect proximity to non-crisis countries

• 0.0158
• 0.0741*

0.3106*

• 0.0665*

0.5276* 0.1385* 0.6192* Indirect proximity to crisis countries 0.0169

• 0.0104

0.0799*

• 0.0651*

0.1557* 0.1610* 0.1874* Key intermediary 0.0442*

• 0.0319*

0.1107*

• 0.0365*

0.1281* 0.0623* 0.1592* Closeness centrality 0.0055

• 0.0825*

0.2949*

• 0.0955*

0.3476* 0.1409* 0.4481* Closeness to network center 0.0481*

• 0.0585*

0.2040*

• 0.0815*

0.2040* 0.0915* 0.2714* Direct current exposure to crisis countries Direct past exposure to non- crisis countries Direct past exposure to crisis countries Indirect proximity to non- crisis countries Indirect proximity to crisis countries Key inter- mediary Closeness centrality Direct past exposure to non-crisis countries 0.0876* Direct past exposure to crisis countries 0.4765* 0.1679* Indirect proximity to non-crisis countries 0.2263* 0.3169* 0.1109* Indirect proximity to crisis countries 0.4082* 0.0300* 0.1973* 0.3291* Key intermediary 0.0730* 0.1037* 0.0996* 0.1095* 0.0313* Closeness centrality 0.2216* 0.2864* 0.1577* 0.3841* 0.1564* 0.3060* Closeness to network center 0.1517* 0.1322* 0.0938* 0.2274* 0.1033* 0.1356* 0.6768*

Effect of Relative Position in the Network

• n Bank Performance - Robustness

(1) (2) (3) (4) (5) (6) Benchmark ROE Drop 1% Bank FE Average for indirect EGBN

• L. key intermediary

0.149* 1.061 0.145* 0.177** 0.174** 0.069 (0.077) (0.878) (0.078) (0.078) (0.078) (0.075)

• L. Key intermediary * Crisis in home country
• 0.538***
• 2.287
• 0.538***
• 0.449**
• 0.431**
• 1.008***

(0.185) (2.493) (0.186) (0.204) (0.205) (0.184)

• L. Closeness centrality
• 1.920***
• 10.509
• 1.912***
• 1.625**
• 1.663**
• 1.786**

(0.649) (7.936) (0.649) (0.757) (0.755) (0.800)

• L. Closeness centrality * Crisis in home country

6.014*** 49.118** 5.968*** 6.215*** 6.219*** 8.211*** (1.352) (19.075) (1.347) (1.575) (1.575) (2.191)

• L. Closeness to network center

0.142** 0.993 0.147** 0.087 0.090 0.040 (0.065) (0.792) (0.065) (0.076) (0.076) (0.070)

• L. Closeness to network center * Crisis in home

country

• 0.627***
• 5.273**
• 0.622***
• 0.760***
• 0.756***
• 0.457**

(0.155) (2.096) (0.154) (0.177) (0.177) (0.233) Observations 8,342 8,341 8,258 8,342 8,322 8,342 R-squared 0.335 0.202 0.334 0.561 0.561 0.340

Effect of Current Direct Exposures on Bank Performance – Alt. specifications

Panel A. (1) (2) (3) (4) (5)

• L. Direct current exposure to non-crisis countries -0.003*

0.001

• 0.001
• 0.000

0.002 (0.002) (0.002) (0.001) (0.001) (0.002)

• L. Direct current exposure to crisis countries
• 0.019***
• 0.014*
• 0.016***
• 0.012***
• 0.004

(0.006) (0.007) (0.004) (0.004) (0.005)

Equity/Assets

0.092*** 0.092*** 0.109*** (0.013) (0.014) (0.017)

Log-assets

0.108*** 0.102*** 0.376*** (0.015) (0.015) (0.089)

Crisis in home country

• 0.827***
• 0.772***
• 0.744***

(0.047) (0.060) (0.073) Type of entity FE no no yes yes yes Specialization FE no no yes yes yes Bank FE no no no no yes Bank nationality FE no yes yes yes no Year FE no no no yes yes Observations 11,874 11,874 9,129 9,129 9,129 R-squared 0.002 0.117 0.324 0.334 0.556

Effect of Past Direct Exposures on Bank Performance – Alt. specifications

Panel B

• L. Direct past exposure to non-crisis countries
• 0.003***
• 0.000
• 0.001
• 0.001

0.004* (0.001) (0.001) (0.001) (0.001) (0.002)

• L. Direct past exposure to crisis countries
• 0.001

0.001

• 0.013***
• 0.012***
• 0.006

(0.004) (0.004) (0.003) (0.003) (0.005)

Equity/Assets

0.093*** 0.093*** 0.109*** (0.013) (0.013) (0.017)

Log-assets

0.108*** 0.104*** 0.378*** (0.014) (0.014) (0.089)

Crisis in home country

• 0.823***
• 0.772***
• 0.764***

(0.047) (0.061) (0.075) Type of entity FE no no yes yes yes Specialization FE no no yes yes yes Bank FE no no no no yes Bank nationality FE no yes yes yes no Year FE no no no yes yes Observations 11,874 11,874 9,129 9,129 9,129 R-squared 0.001 0.117 0.324 0.334 0.556