Interbank Networks, Contagion, and Common Shocks

Interbank Networks, Contagion, and Common Shocks

Co-Pierre Georg

Interdisciplinary Group of Complex SystemsUniversidad Carlos III de Madrid

and

Graduate School “Foundations of Global Financial Markets”Friedrich-Schiller-Universitat Jena

CABDyN Complexity Center, Saıd Business School, Oxford UniversityOxford, 02/07/2012

Co-Pierre Georg (UC3M & Jena) Interbank Networks, Contagion, and Common Shocks Oxford, 02/07/2012 1 / 28

Motivation

The financial system has become increasingly interconnected and complex

Supervision of individual financial institutions insufficient⇒ Network structure of interconnections matters

Systemic risk takes various forms and is highly dynamic⇒ Better understanding needed to safeguard financial stability


Motivation

The financial system has become increasingly interconnected and complex

Supervision of individual financial institutions insufficient⇒ Network structure of interconnections matters

Systemic risk takes various forms and is highly dynamic⇒ Better understanding needed to safeguard financial stability

This talk: agent-based models and network theory to address thesequestions


A Simple Bank Balance Sheet

Liabilities Assets

D

L

LC

BC

I

E

F


The Interconnectedness of the Financial System Increased

Jan 1999 Jan2001 Jan2003 Jan2005 Jan2007 Jan2009 Jan2011

34

56

7

Date

Volu

me

[Tri

llio

n E

UR

]

Figure: Deposit liabilities of euro area MFIs vs. other euro area MFIs, outstandingamounts at the end of the period, neither seasonally nor working day adjusted. Source:ECB Statistical Data Warehouse.



Figure: Left: Largest links on e-MID on 3 January 2007. Source: Gabrieli (2010). Right:Net pairwise correlation of stocks on 16 September 2008. Source: Diebold and Yilmaz(2011).



Figure: Left: Cross-border debt assets (from CPIS and BIS locational). Source: Kubelecand Sa (2010). Right: Cross-border banking (BIS locational). Source: Minoiu and Reyes(2011).



1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

010

020

030

040

050

060

0

Date

Volu

me

[Tri

llio

n U

SD

]

Foreign exchangeInterest rateEquity−linkedCommodityCDS

Figure: Global over-the-counter derivatives markets, notional amounts of contractsoutstanding. Source: IMF


Indirect Linkages Amplify the Risk of Fire-sales

Figure: U.S. Mortgage-Related Securities Issuance. Source: Gorton and Metrick (2010)

.


Indirect Linkages Amplify the Risk of Fire-sales

Figure: Forced Sales Discounts and Time Between Sale and Event. Source: Campbell,Giglio and Pathak (2012)

.


Do We Need Yet Another Paper?

Literature on Financial Networks

Allen and Gale (2000), Freixas et al. (2000)

Haldane and May (2011), Gai et al. (2011), Gai and Kapadia (2008)

Becher et al. (2008), Gabrieli (2011), Chang et al. (2008), Brink and Georg(2011), Markose et al. (2010)

Literature on Fire-sales

Shleifer and Vishny (1992): specialised asset holders are simultaneously indistress and sell to non-specialists

Allen and Gale (1994): endogenous market participation

Literature on Multi-Agent Models:

Iori et al. (2006), Nier et al. (2007)

However: risk-free investments, no central bank, mechanistic agentbehaviour, “fine-tuning”


What is Systemic Risk?

Definition by impact

FSB definition: “a risk of disruption to financial services that is (i) caused byan impairment of all or parts of the financial system and (ii) has the potentialto have serious negative consequences for the real economy.”

Definition by cause

B1

B2 B3 A1

A2

B1

B2

⇒

B1

B2

Figure: Left: direct connections (counterparty risk, contagion). Right: indirectconnections (common shocks, fire-sales).


Focus of this Paper

Complete dry-up of interbank markets in September 2008, central banks wereforced to unprecedented non-standard measures

⇒ Q1: Can central banks stabilize interbank markets?

Systemic risk requires macroprudential oversight in addition tomicroprudential supervision

⇒ Q2: What are robust network structures?

Different forms of systemic risk can act differently on the financial system⇒ Q3: What are the optimal policy responses?


Microfoundations of Banks Determine Model

Liabilities Assets

DHouseholds



Liabilities Assets

D

LC

Households

CentralBank



Liabilities Assets

D

LC

I

E

Households

CentralBank

Firms

CentralBank



Liabilities Assets

D

L

LC

I

E

L

Households

CommercialBanks

CentralBank

Firms

CentralBank

CommercialBanks



Liabilities Assets

D

L

LC

BC

I

E

L

Households

CommercialBanks

CentralBank

Firms

CentralBank

CommercialBanks


Agent Behaviour has to be Motivated

Banks optimize their portfolio structure and -volume according to CRRApreferences

u =1

1− θ

(

V (1 + λµ−1

2θλ2σ2)

)(1−θ)

where θ is risk-aversion parameter

Update algorithm for k = 1, . . . ,N banks and t = 1, . . . , τ update steps:

1 Obtain returns on investments, pay interest on deposits

2 Deposit in- and out-flows, required reserves

3 Settle interbank loans

4 Determine new investment level

5 Settle liquidity position

6 Pay dividends



λ

Liabilities Assets

D

L

LC

BC

I

E

L

Households

CommercialBanks

CentralBank

Firms

CentralBank

CommercialBanks


Interbank Loans Form a Network Structure

Figure: Different scale free networks


Model Parameters

Sektor Parameter

Households deposit fluctuations γFirms credit success probability pf , realized

credit return (ρ+f , ρ−

f )Commercial banks deposit interest rate rd , dividend level βk ,

expected credit success probability pb, expected creditreturn (ρ+b ,ρ

−

b ), risk aversion parameter θCentral bank main refinancing rate rb, minimum reserve

requirement r , quality of securities αk

Network parameters

number of banks N, level of interbank connections connLevel

Simulation parameters

number of update steps τ , number of simulations numSimulations


Central Bank Liquidity Stabilizes in the Short-Run...

0 250 500 750 1000

00

20

40

60

80

100

120

140

Time t

# of

Act

ive

Ban

ks

cbActivity=1.0cbActivity=0.8cbActivity=0.5cbActivity=0.45cbActivity=0.4cbActivity=0.2cbActivity=0.0

Figure: The effect of central bank activity αk on financial stability in a crisis scenario

(ρ+f = 0.09, ρ−f = −0.08)


...but the Effect is Non-Monotonic

0 250 500 750 1000

00

20

40

60

80

100

120

140

Time t

Inte

rban

k lo

an v

olum

e L

cbActivity=1.0cbActivity=0.8cbActivity=0.5cbActivity=0.45cbActivity=0.4cbActivity=0.2cbActivity=0.0

Figure: The effect of central bank activity αk on interbank liquidity in a crisis scenario

(ρ+f = 0.09, ρ−f = −0.08)


Central Bank Liquidity Stabilizes in the Short-Run

Lesson 1:

Central bank liquidity provision has non-linear effect on financial stability⇒ Close threshold value, small changes have significant impact⇒ Away from threshold value, even large changes can be ineffective

Stabilizing effect in the short-run only

Abundant central bank liquidity crowds out interbank liquidity


Some Network Structures are More Resilient Than Others

0 250 500 750 1000

00

20

40

60

80

100

120

140

Time t

# of

Act

ive

Ban

ks

connLevel=1.0connLevel=0.8connLevel=0.6connLevel=0.4connLevel=0.2connLevel=0.0

Figure: The impact of the network topology on financial stability in a normal scenario(ρ+f = 0.09, ρ−f = −0.05) in a random network.



0 250 500 750 1000

00

20

40

60

80

100

120

140

Time t

# of

Act

ive

Ban

ks


Figure: The impact of the network topology on financial stability in a crisis scenario(ρ+f = 0.09, ρ−f = −0.08) in a random network



0 250 500 750 1000

00

20

40

60

80

100

120

140

Time t

# of

Act

ive

Ban

ks

m=1m=2m=4m=10

Figure: The impact of the network topology on financial stability in a crisis scenario(ρ+f = 0.09, ρ−f = −0.08) in a BA network



0 250 500 750 1000

00

20

40

60

80

100

120

140

160

Time t

Inte

rban

k lo

an v

olum

e L


Figure: The impact of the network topology on interbank liquidity in a crisis scenario(ρ+f = 0.09, ρ−f = −0.08) in a random network



Lesson 2:

Network structure matters in crises

Relationship between financial stability and interconnectedness in randomnetworks is non-monotonic

Scale-free networks tend to be more stable than random networks

Interbank networks are robust-yet-fragile⇒ Size of endogenous fluctuations matter


Different Forms of Systemic Risk Require Different Answers

0 250 500 750 1000

00

20

40

60

80

100

120

140

Time t

# of

Act

ive

Ban

ks

Interbank contagionCommon shock ACommon shock B

Figure: The impact of different forms of systemic risk on financial stability in a crisisscenario (ρ+f = 0.09, ρ−f = −0.08) in a random network (connLevel=0.8)



0 250 500 750 1000

20

40

60

80

100

120

140

160

20

40

60

80

100

120

140

160

Time t

Inte

rban

k lo

an v

olum

e L

Interbank contagionCommon shock ACommon shock B

Figure: The impact of different forms of systemic risk on financial stability in a crisisscenario (ρ+f = 0.09, ρ−f = −0.08) in a random network (connLevel=0.8)



Lesson 3:

Common shocks can pose greater threat to financial stability

Contagion mainly reduces liquidity available in the system

Common shock mainly reduces banking capital and increases (relative) sizeof endogenous fluctuations

⇒ Different optimal responses, for different forms of systemic risk

⇒ Implications for financial Regulation



Lesson 3:

Common shocks can pose greater threat to financial stability

Contagion mainly reduces liquidity available in the system

Common shock mainly reduces banking capital and increases (relative) sizeof endogenous fluctuations

⇒ Different optimal responses, for different forms of systemic risk

⇒ Implications for financial Regulation

Thank you!


References

Francis Allen and Douglas Gale. Financial contagion. Journal of Political Economy, 108:1–33,2000.

Christopher Becher, Stephen Millard, and Kimmo Soramaki. The network topology of chapssterling. Bank of England working papers 355, Bank of England, 2008.

Nicola Brink and Co-Pierre Georg. Assessing systemic risk in the south african interbank market.Special Note in the Financial Stability Review April, South African Reserve Bank, 2011.

Eui Jung Chang, Eduardo Jose Araujo Lima, Solange M. Guerra, and Benjamin M. Tabak.Measures of interbank market structure: An application to brazil. Brazilian Review of

Econometrics, 28(2):163–189, 2008.

Silvia Gabrieli. The microstructure of the money market before and after the financial crisis: anetwork perspective. CEIS Research Paper 181, Tor Vergata University, CEIS, 2011.

Prasanna Gai and Sujit Kapadia. Contagion in financial networks. Discussion Paper 1231, Bankof England, 2008.

Prasanna Gai, Andrew Haldane, and Sujit Kapadia. Complexity, concentration and contagion.Journal of Monetary Economics, 58(4), 2011.

Andrew G. Haldane and Robert M. May. Systemic risk in banking ecosystems. Nature, 469:351–355, 2011.

G. Iori, S. Jafarey, and F.G. Padilla. Systemic risk on the interbank market. Journal of Economic

Behavior & Organization, 61(4):525–542, 2006.

Erland Nier, Jing Yang, Tanju Yorulmazer, and Amadeo Alentorn. Network models and financialstability. Journal of Economic Dynamics and Control, 31:2033–2060, 2007.

Interbank Networks, Contagion, and Common Shocks

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