Socio-Economic Complexity Workshop Leiden 27 March 2015 Andrew G Haldane On Microscopes and...

Socio-Economic Complexity Workshop

Leiden

27 March 2015

Andrew G Haldane

On Microscopes and Telescopes

The story so far …

2

• Biggest crisis since the 1930s

• Existing models no use in explaining it – before, during or after

• Existing policies ineffective in preventing it

• Network/complexity models have risen in prominence

• Complexity language enters regulatory lexicon – tipping points,

contagion cascades etc

• New regulatory architecture put in place

• Eg, “too big to fail” and capital surcharges for “superspreader”

banks

3

Forecasting GDP Growth

2008Q1

2008Q2

2008Q3

2008Q4

2009Q1

2009Q2

2009Q3

2009Q4

2010Q1

2010Q2

2010Q3

2010Q4

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

2014Q1

2014Q2

2014Q3

2014Q4-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

May 2008 May 2009 May 2010 May 2011 Latest data

Per cent change oya

The Macro-Financial Policy Architecture

4

Old

Micro-Prudential

Monetary Policy

Global Architecture

• How to make sense of this new architecture?

The Macro-Financial Policy Architecture

5

New Old

Micro-Prudential

Macro-Prudential

Monetary Policy

GlobalArchitecture

Micro-Prudential

Monetary Policy

Global Architecture

Architecture of Complexity

6

• Complex system: whole ≠ sum of parts

• Complex dynamics: non-linearities, emergent behaviour,

fat tails

• Hierarchical structures may re-emerge for evolutionary

reasons – “decomposable” systems (Simon)

• Is that true of socio-economic systems?

Network economies of scale/scope foster tight-coupling Policy moral hazard prevents Darwinian evolution It may even encourage tight coupling – the “Doom Loop”

Socio-economic systems may be “non-decomposable”

Complex “System of Systems”

A Complex System of Systems?

7

• Complex System of Systems: nested set of sub-systems, themselves individually complex

• Eg, “layered” networks

Risk much greater than implied by looking at each layer “Missing a layer” poses real risks when assessing risks The more complex the layers, and the higher their correlation,

the more complex the system Tinbergen rule (for complex systems): as many policy

instruments as there are complex layers

Layered network

7

Layer 1

Layer 2

Layer 3

Simulating Complexity

9

Chart 1: Joint Distributions of Simulated System of systems(a)

• An uncorrelated simple versus correlated complex system of systems

Simulating Complexity

10

-2

-1

0

1

2

-2 -1 0 1 2

Chart 1: Joint Distributions of Simulated System of systems(a)

… adding a complex layer …

The Macro-Financial System of Systems

11

Complexity of Individual Banks - 2006

12

Size of Balance Sheet(b) Nominal Value of Derivatives(c)

Number of Legal Entities(d) Trading Assets (% of Total Assets) (e)

Average$1,350bn

Average$19Tr (notional)

Average328 entities

Average22%

Complexity of Individual Banks - 2013

13

Size of Balance Sheet(b) Nominal Value of Derivatives(c)

Number of Legal Entities(d) Trading Assets (% of Total Assets) (e)

Average$1,758bn

Average$31Tr (notional)

Average330 entities

Average19%

Bank Complexity and Performance

14

Source: True Cost of complexity in the banking sector’, Simplicity Consulting (2012).

Hig

h pe

rfor

m-

ance

Low

per

form

ance

Simple Complex

Financial System Complexity

15

Interbank exposures network (2013)

Financial System Complexity – Non-Banks

16

Macro-Economic Complexity

17

Flow of Funds, United Kingdom (1978)

125%Household

Assets

?RoW

Assets

6%

?RoW

Liabilities

24%UK Building

societies

69%Foreign Banks

22%UK Other Banks

(incl. Scottish Banks)

2%

25%Clearing Banks

29%Insurance

Companies

18%Pension Funds

2%

4%

55%Corporate

Assets

?Government

Assets

98%CorporateLiabilities

47%Government

Liabilities

38%HouseholdLiabilities

Bank of England

UTs

Inv. Trusts

Finance Houses

245%Household

Assets

250%RoW

Assets

305%RoW

Liabilities


30%RoW Inv.

Banks

110%Corporate

Assets

25%Gov.

Assets

65%Government

Liabilities


180%UK Universal Banks

60%UK Domestic

Banks

35%UK Other

Banks

20%SPVs

25%Bank of England

15%CCPs

50%Unauthorised

funds

5%PE Funds

5%ETFs

5%Investment Trusts

5%Finance Companies

50%Hedge Funds

40%OEIC and

AUTs

80%Pension Funds

80%Insurance Companies

15% RoW Other Banks


18


• Source: ONS, IMA, FCA, BVCA, Financial statements, Regulatory Returns, Bank calculations. Notes: Balance sheets are sized net of derivatives and intrabank exposures and expressed as a % of GDP.

245%Household

Assets

250%RoW

Assets

305%RoW

Liabilities


30%RoW Inv.

Banks

110%Corporate

Assets

25%Gov.

Assets

65%Government

Liabilities



60%UK Domestic

Banks

35%UK Other

Banks

20%SPVs

25%Bank of England

15%CCPs

50%Unauthorised

funds

5%PE Funds

5%ETFs

5%Investment Trusts

5%Finance Companies

50%Hedge Funds

40%OEIC and

AUTs

80%Pension Funds


15% RoW Other Banks


19



245%Household

Assets

250%RoW

Assets

305%RoW

Liabilities


30%RoW Inv.

Banks

110%Corporate

Assets

25%Gov.

Assets

65%Government

Liabilities



60%UK Domestic

Banks

35%UK Other

Banks

20%SPVs

25%Bank of England

15%CCPs

50%Unauthorised

funds

5%PE Funds

5%ETFs

5%Investment Trusts

5%Finance Companies

50%Hedge Funds

40%OEIC and

AUTs

80%Pension Funds


15% RoW Other Banks


20



Global Complexity

21

0

5

10

15

20

25

0

20

40

60

80

100

120

140

160

180

1870 1900 1914 1930 1960 1985 1990 2005

External financial assets/GDP (RHS)

Trade/GDP (LHS) (a)

Per centPer cent

The Growth in Global Trade and Finance

Global Trade Complexity

22

1995 2013

Global Financial Complexity

23

The Global Banking System 1980 2007

Economic and Financial Complexity

24

GDP growth Credit Growth

Economic and Financial Complexity

25

Equity Returns Rice Prices

Public Policy Implications

26

• How does this help us assess the new macro-financial architecture?

Data

Models

Policy Design

Data Gaps

27

green=Available data, amber=Limited availability, and red=no data availability

245%Household

Assets

250%RoW

Assets

305%RoW

Liabilities


30%RoW Inv.

Banks

110%Corporate

Assets

25%Gov.

Assets

65%Government

Liabilities



60%UK Domestic

Banks

35%UK Other

Banks

20%SPVs

25%Bank of England

15%CCPs

50%Unauthorised

funds

5%PE Funds

5%ETFs

5%Investment Trusts

5%Finance Companies

50%Hedge Funds

40%OEIC and

AUTs

80%Pension Funds


15% RoW Other Banks

Modelling Banking Interactions

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Total assets in the system generated from RAMSI

Simulating Monetary and Macro-Prudential Policy

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0.4

0.5

0.6

0.7

0.8

0.9

1

0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Variance of nominal GDPVariance of nominal GDP

Varia

nce

of sp

read

s

A) NormalisedB) Optimised monetary policyC) Optimised monetary policy with spreadD) Macroprudential policy

A

B

C

D

Macro-Financial Policy Design

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• TEXT [ARIAL 20]

2009 Q1

0% 15%

Source: Data are based on the Bank of England’s internal Product Sales Database collected by the FCA.

Calibrating Macro-Prudential Policy• Loan-to-income multiple ≥ 4.5

2009 Q2

0% 15%

Loan-to-income multiple ≥ 4.5

2009 Q3

0% 15%


2009 Q4

0% 15%


2010 Q1

0% 15%


2010 Q2

0% 15%


2010 Q3

0% 15%


2010 Q4

0% 15%


2011 Q1

0% 15%


2011 Q2

0% 15%


2011 Q3

0% 15%


2011 Q4

0% 15%


2012 Q1

0% 15%


2012 Q2

0% 15%


2012 Q3

0% 15%


2012 Q4

0% 15%


2013 Q1

0% 15%


2013 Q2

0% 15%


2013 Q3

0% 15%


2013 Q4

0% 15%


2014 Q1

0% 15%


2014 Q2

0% 15%


Macro-Prudential Policy

Global Financial Architecture

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-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1961 1968 1976 1984 1992 1999 2007

FranceGermanyUSA

Correlation coefficient

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1994 1997 2000 2003 2006 2009 2012

UK - US

UK - euro area

Correlation coefficient

Chart 20: Correlation of 10 year bond yields and equity prices

• Co-ordinating global macro-prudential policy?

Conclusion

54

… unfinished business …

Socio-Economic Complexity Workshop Leiden 27 March 2015 Andrew G Haldane On Microscopes and...

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