1 1 1 Frank Smets Directorate General - Research Macro Financial Modeling Group Conference, Chicago 2-3 May 2013 Panel discussion on Policy Perspectives.

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1

Frank SmetsDirectorate General - Research

Macro Financial Modeling Group Conference, Chicago

2-3 May 2013

Panel discussion on “Policy Perspectives on Systemic Risk

Measurement”

Rubric

Systemicdimension

Institution dimension

Micro-prudentialSoundness of

individual banks

Monetary PolicyPrice Stability

Macro-prudential policy

Financial stability

New institutional set-up in the euro area

2

European Systemic Risk Board (ESRB)

European Financial

Authorities (EBA, ESMA,

EIOPA)

ECBEU

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Two perspectives

• Time-series perspective: Smoothen the financial cycle Finance is pro-cyclical: Why? Endogenous

credit constraints and liquidity creation? Incentives? Expectations?

How to manage the financial cycle? Need tools to analyse and interpret the build-

up and unravelling of financial imbalances. • Cross-section perspective: Improve the

resilience of the financial system Finance is inherently fragile: Why? Leverage,

liquidity/maturity/risk transformation, interconnectedness, complexity.

How to make the financial system more resilient?

Need tools to understand/predict spill-overs, contagion, negative feedback loops.

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Quantity versus price-based indicators

• Quantity-based indicators have performed better in signalling the building up of financial imbalances E.g. Credit-to-GDP ratio: Borio, Alessi-Detken,

Schularick and Taylor; Non-core liabilities as fraction of M2, broker dealers’leverage: Adrian & Shin.

More useful for ex-ante leaning against the financial cycle?

• Prices sent the wrong signals ex ante, partly due to what has been called the “volatility paradox”, but are better at capturing the unravelling of the imbalances: E.g. Marginal Expected Shortfall, CoVar, Bank

Stability Index; Network analysis; etc More useful for ex-post interventions?

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Early warning signal models “Global” credit gap and optimal early warning threshold

• —— De-trended private credit-to-GDP ratio (GDP-weighted average across countries)• ––––– “Optimal” signal threshold

(Q1 1979 – Q4 2012; percentages)

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Largest increase in leverage in OFIs

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ECB Systemic Risk IndicatorProbability of two or more banks defaulting simultaneously within next 2 years

Source: ECB

Lucas, A., Schwaab, B., and X. Zhang (2012), ECB WP

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Exemplified by: Strong correlation between bank CDS and sovereign CDS in the euro area

Sovereign and bank CDS premiaUnited States Euro area

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150 200 250 300 350 400 450 500

bank

CD

Ss

sovereign CDSs

2010 Q1

2010 Q2

2010 Q3

2010 Q4

2011 Q1

2011 Q2

2011 Q3

2011 Q4

2012 Q1

2012 Q2

2012 Q3

2012 Q4

2013 Q1

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150 200 250 300 350 400 450 500

bank

CD

Ss

sovereign CDSs

2010 Q1

2010 Q2

2010 Q3

2010 Q4

2011 Q1

2011 Q2

2011 Q3

2011 Q4

2012 Q1

2012 Q2

2012 Q3

2012 Q4

2013 Q1

Sources: Thomson Reuters and ECB calculations.In: ECB (2013): Report on Financial Integration in Europe.

Negative feedback loop between banks and sovereigns

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Challenge

• Link time-series and cross-section perspectives: Why and in what circumstances do credit

booms go hand in hand with greater leverage, liquidity mismatch, interconnectedness and complexity?

Why and in what circumstances are credit booms associated with more risk-taking on the financial sector’s asset side?

• Need better time series data and measurement of leverage, liquidity mismatch, interconnectedness and complexity

• Need dynamic macro models that incorporate the building up of systemic risk and the non-linear feedback mechanisms that kick in in crises.

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MaRs: ESCB research network

Three work streams:1.Macro-financial models linking financial stability and the performance of the economy2.Early warning systems and systemic risk indicators3.Assessing contagion risks

Interim report available on the ECB’s website.