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Third International Seminar on Early Warning and
Business Cycle Indicators
Moscow, 17-19 November 2010
“Early warning indicators to predict financial crises”
Gert Schnabel
Monetary and Economic Department,
Bank for International Settlements
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Early warning indicators in the context of BIS research
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Special focus of the BIS: financial and monetary stability rather than
cyclical stability
Policy orientation >
- macroprudential policy (stability of the financial system)
- monetary policy (price stability)
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Special focus of the research on stability of the financial system:
- credit and asset prices
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Business cycle and financial cycle go together and interact but are no the
same
- credit cycles are typically longer
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Some BIS publications
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Selection of important BIS research pieces (upon which the following is
based)
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http://www.bis.org/publ/work114.pdf?noframes=1
Borio, Lowe – Asset prices, financial and monetary stability: exploring the
nexus (2002)
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http://www.bis.org/publ/work157.htm
Borio, Lowe – Securing sustainable price stability: should credit come back
from the wilderness? (2004)
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http://www.bis.org/publ/qtrpdf/r_qt0903e.pdf
Borio, Drehmann – Assessing the risk of banking crises – revisited ( Quarterly
review, Q1 2009)
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The early warning indicator concept (BIS)
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…Drawing on Kaminsky/Reinhart (1999) methodology: working with
noise/signal ratios and threshold values
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… but deviating in various respects
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Specific features of the early warning indicator concept:
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…working with cumulative changes (gaps)
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… working with real-time data
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… working with combination of indicators
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… working with multiple horizons
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Which Indicators ?
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…(private) credit/GDP and asset prices
… asset prices > build-up of bubbles
…(private credit)/GDP > absorption capacity/leverage of the private
sector
… asset price candidates: equities, property prices, aggregate asset
prices
… in real terms (to capture the excess inflationary development of
assets)
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Gap concept:
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deviation of credit/GDP respectively property prices from their trend
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trend: one-sided HP-filter with a large lambda, ie very smooth trends as
the target is to capture the gradual building up of imbalances
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smooth trends are also justified by the fact that credit cycles tend to be
rather long
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Real time data:
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Trend calculation based on the data available at the period to which they
refer, ie
- no later data revisions
- no adjustment of the trend based on data referring to future periods
Idea that at a specific point of time, assessments of potentially upcoming
crisis must be taken on the then available information
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Combination of indicators
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Signaling of crisis tested
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… by combining indicators
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… and by seeking to optimise the threshold values
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Multiple horizons:
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… aim is to predict the occurrence of a crisis not the exact timing
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… leading properties normally comparably long
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Noise signal ratio (NSR)
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T1 (error type 1) : crisis occurred but was not predicted
T2 (error type 2): crisis was predicted but did not occur
NSR = T2 / 1 – T1
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Minimising NSR ?
Target function (ie threshold setting) depends on the weighting of T1 and
T2
Chosen: minimising under the condition that a minimum number of
crises has been correctly predicted
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General results
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Credit/GDP gap outperforms the other indicators
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Multiple gaps outperform single indicators
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Results are not extremely threshold sensitive
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> policy recommendation: not considering the gaps and its thresholds as
a strict rule but as guidelines
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Possible extensions
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adding the external component
- external exposure of banks
- property prices in the exposure countries
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Use of indicators in the current discussion
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Basel III discussion
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Countercyclical buffer: recommendation to use the credit/GDP gap as
guide for the buffer
….but not without judgment
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and..
taking into account cross-border banking activities: weighted Credit/GDP
gaps of the countries in which the banks operate
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Data needs
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Domestic private credit
in particular: long series on broad credit in harmonised definition
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Asset prices
in particular: long series for property prices, especially for Emerging
market economies
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Cross – border banking data
in particular long series
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