Download Systemic indicators

Systemic indicators
Developing inputs on system-wide risks for financial
stability analysis and macroprudential policy
Paul Van den Bergh
Head of Information, Statistics and Administration
Monetary and Economic Department
IMF-FSB Users Conference, Washington DC, 8-9 July 2009
Views expressed are those of the author and not necessarily those of the BIS
or its associated organisations
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What are systemic indicators?
 Early warning indicators
 Financial stability/vulnerability indicators
 Financial soundness indicators
 Macro-prudential indicators
 Mixture of “individual” indicators and “composite”
indicators
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Commonly used variable in financial stability
reports
 Real economy: GDP, government fiscal position, inflation
 Corporate sector: debt to equity, earnings (to interest and
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principal expenses),fx exposure, defaults
Household sector: assets and liability positions, income,
consumption, debt service levels
External sector: (real) exchange rates, fx reserves,
CA+capital flows, maturity/currency mismatches
Financial sector: money, (real) interest rates, bank credit,
bank leverage, NPLs, CDS premia, capital adequacy,
liquidity ratio, credit ratings, sectoral/regional concentration
of exposures
Financial markets: stock index, corporate bond spread,
market liquidity, volatility, house prices
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Macroprudential policy/framework
 Much work in Basel, still confusion over its definition
 Two features
1. Focus on financial system as a whole
2. Treat aggregate risk as dependent on collective behaviour of
financial institutions (endogenous)
 Contrast with microprudential which focuses on individual
institutions and treats aggregate risk as exogenous
 For 1: think of financial system as portfolio of securities
with each security representing financial institution
 For 2: think of link credit extension to economic activity, to
asset price inflation, to increase in valuation of collateral to
credit extension …
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Macroprudential policy/framework: dimensions
 Risk distribution at a given point in time (cross-sectional)
• Correlation of exposures across institutions (direct or through
linkages)
• Contribution of individual institution to system-wide risk
• Likelihood of failure if others face distress at same time
• Vulnerability to risk concentrations even if individual
institutions are diversified
 Risk evolution over time (time dimension)
• How can system-wide risk by amplified by interaction
financial system and real economy?
• Procyclicality
• Impact of macroeconomic sources of risk: asset prices,
credit, leverage
 Important implications for calibration of prudential tools
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Macroprudential policy/framework: cross-sectional
 Measure likelihood of systemic event at given point in time
 Use techniques applied to portfolios of securities
 Data required
• Size of institutions
• Institution’s probability of default
• Loss-given default in each case
• Correlation of defaults
 Information can be collected from supervisory
assessments, prices of bank equity and debt
 Overall level of systemic risk increases with institutions’
exposures to common risk factors
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Price of insurance against systemic distress1
By financial segment2
Importance of a common driver3
1
Based on credit default swap (CDS) spreads for 10 commercial and eight investment banks
headquartered in North America (NA), 16 universal banks headquartered in Europe and 14 insurance
companies headquartered in the United States and Euro pe; in per cent. 2 The “Total” line plots the risk
neutral expectation of credit losses that equal or exceed 5% of the four financial segments ’ combined
liabilities in 2008 (per unit of exposure to these liabilities). Risk neutral expectations comprise expectations
of actual losses and preferences. The shaded areas portray how the total is allocated among the four
financial segments. The vertical line marks September 2008, the month in which Lehman Brothers filed for
Chapter 11 bankruptcy protection. 3 The average share of institutions’ asset return volatility accounted for
by a risk factor that is common to all four financial segme nts.
Sources: Bankscope; Bloomberg; Markit; BIS calculations.
Graph III.1
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Macroprudential policy/framework: cyclical
 Countercyclical capital requirements (CR)
• Choose indicator that signals time to build up and release
capital buffer
• Choose formula to determine CR when indicator changes
• Adjust actual CR (rule-based or discretionary)
 Possible indicators
• Credit spreads
• Real asset prices
• Composite indicator combining credit/GDP ratio and real
asset prices
• Example for US
• Variable presented as deviations from long-term average
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Alternative indicators and charge-off rate in the United States
1
Loans and leases removed from the books and charged against loss reserves, as a percentage of average total loans. 2 Deviation
of long-term BBB-rated corporate bond spreads from their long-term average, in basis points. 3 Exponentially weighted five-year
average real credit growth minus its 15-year rolling average, in percentage points. 4 Deviation of each variable from its one-sided
long-term trend (that is, a trend determined only from information available at the time assessments are made); credit-to-GDP ratio in
percentage points, property prices in per cent.
Sources: Moody’s; national data; BIS calculations.
Graph VII.B.1
Reprioritisation of Financial Soundness Indicators?
 Significant statistical project developed with wide
consultation
 Large number of participating countries
 Distinction core and encouraged sets?
 Core set
• various ratio’s of aggregate balance sheet items of deposit
takers
• concept of consolidation not clearly understood
• difficult to integrate data with other banking datasets
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Reprioritisation of Financial Soundness Indicators?
 Encouraged set
• Includes indicators on indicators for other sectors, financial
markets and real estate price indices
• More emphasis on non-bank financial sectors?
• Financial positions of other sectors through financial
account/balance sheet approach?
• More emphasis on housing and housing finance indicators
(methodology on residential real estate price indices being
developed by IWGPS/Eurostat)?
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Conclusions
 Need to be clearer about what we want to measure
(individual elements of “financial situation”, composites,
leading indicators?)
 Specific set of information requirements for
macroprudential policy/framework (cross-sectional and
cyclical analysis of aggregate risk; mixture of micro and
macro data)
 Possible to improve FSI’s both in terms of methodology
and content (distinction core vs encouraged, better
coverage of non-bank sector, more “agile” methodology)
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