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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

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)

Special focus of the research on stability of the financial system: - credit and asset prices

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

Selection of important BIS research pieces (upon which the following is based)

http://www.bis.org/publ/work114.pdf?noframes=1 Borio, Lowe – Asset prices, financial and monetary stability: exploring the

nexus (2002)

http://www.bis.org/publ/work157.htm Borio, Lowe – Securing sustainable price stability: should credit come back

from the wilderness? (2004) 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)

…Drawing on Kaminsky/Reinhart (1999) methodology: working with noise/signal ratios and threshold values

… but deviating in various respects

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Specific features of the early warning indicator concept:

…working with cumulative changes (gaps) … working with real-time data … working with combination of indicators … working with multiple horizons

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Which Indicators ?

…(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:

deviation of credit/GDP respectively property prices from their trend 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 smooth trends are also justified by the fact that credit cycles tend to be

rather long

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Real time data:

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

Signaling of crisis tested … by combining indicators … and by seeking to optimise the threshold values

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Multiple horizons:

… aim is to predict the occurrence of a crisis not the exact timing … leading properties normally comparably long

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Noise signal ratio (NSR)

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

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

Credit/GDP gap outperforms the other indicators Multiple gaps outperform single indicators Results are not extremely threshold sensitive

> policy recommendation: not considering the gaps and its thresholds as a strict rule but as guidelines

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Possible extensions

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

Basel III discussion

Countercyclical buffer: recommendation to use the credit/GDP gap as guide for the buffer

….but not without judgment

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

Domestic private credit

in particular: long series on broad credit in harmonised definition Asset prices

in particular: long series for property prices, especially for Emerging market economies

Cross – border banking data

in particular long series