Search for a Metric for Financial Stability

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Search for a Metric for Financial Stability By
C.A.E. Goodhart It is easier to define,
measure, model, analyse and control price
stability than to do so for financial
stability. We (Dimitri Tsomocos, Oriol Aspachs,
Ton Sunirand, Lea Zicchino) have an ongoing
program of work to make a start on such
issues. In several ways the problem of
measurement should have priority. Economics is a
quantitative social science. Need to compare and
analyse. The current unit of measurement is the
event of a bank crisis, but problems of timing
of onset, duration, choice of event, intensity,
and throwing away non-crisis data. Our work
began with an attempt to model financial
fragility, GST, A model to analyse financial
fragility, Economic Theory. When we came to
take this model to the data, first by simulation,
(GST, Journal of Financial Stability, 2004), and
then by calibration, (GST, Annals of Finance,
2006), two key factors were what was effect of
shock on (i) bank profitability, (ii) default
rates of banks and their customers.
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A Framework for Financial Stability
Contrasts between Price and Financial Stability
Price Stability Financial Stability
a) Measurement and Definition Yes, subject to technical queries Hardly, except by its absence
b) Instrument for control Yes, subject to lags Limited, and difficult to adjust
c) Accountable Yes Hardly
d) Forecasting Structure Central tendency of distribution Tails of distribution
e) Forecasting Procedure Standard Forecasts Simulations or Stress Tests
f) Administrative Procedure Simple Difficult
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A typical example from our latest paper (AGTZ
2006) shows the effects of a change in default
penalties on interest rates, and bank profits,
capital, capital ratio and repayment rates
(default probabilities). In this example
repayment rates go up, (i.e. default
probabilities decline), whereas profits decline,
primarily because banks choose a safer, less
risky investment strategy. While normally the
expectation is that profitability and default
probabilities are inversely correlated, this is
not always the case, especially when banks (are
induced to) choose riskier (or less risky)
strategies. Greater risk implies profits and PD
should rise. Indeed, in our data base, the
correlation between the change in equity
values, (our proxy measure of profitability) and
estimated P.D. is only about -0.32 on a
quarterly, and -0.61 on an annual, basis, being
the average of the simple correlations in our
seven countries.
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So we felt forced to move to a two factor model,
profitability and PD, regretfully so since we are
seeking a single metric, and to achieve that from
a 2 factor model requires some (estimated) factor
weighting. Initially tried data from bank
accounts, e.g. profits, NPLs, write-offs, etc.,
but did not work well- (i) Accounting
inconsistencies, both between countries and over
time (ii) Manipulation and smoothing (iii) Long
lags in reporting, especially write-offs. So
we turned to market variables, equity values (as
a proxy for profits) and PD, with the latter
taken from the IMF. Note that estimate of PD
incorporates both equity valuation and market
volatility. So why is it not a sufficient
statistic in itself?
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Answers- (i) Low correlation, already
noted (ii) What are the relevant weightings
e.g. Vol Ai f (Vol Market, Vol Sector, Vol
Company covariances) (iii) Empirical
Test. Our procedure was to examine
relationships between GDP, our measure of social
welfare, inflation, PD of banking sector and
equity values of banking sector. Initial
exercises, however, indicated that both PD and
changes in Eq were threshold variables. They
only adversely affected GDP if worse than some
level. Theshold levels chosen to maximise fit
(n.b. small data set, few countries, short
period). Anyhow individual and panel VAR, with
four variable VAR and plus property prices, and
plus interest rates as well.
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Data set includes Finland, Germany, Japan,
Korea, Norway, Sweden and UK over period 1990, Q4
2004, Q4, (n.b. bank equity values only
available for Norway and Finland from 1996
onwards, and for Sweden from 2000
onwards). Results Surprisingly good for
Panel Supportive, but not totally so for
individual countries. What does this
suggest- (a) for weighting of Eq and
PD? (b) for effects on GDP of individual
countries?
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Can we get a single metric, comparable over time
and across countries for financial
stability? Yes, conditional on- (i) methods
(transformation, etc.) used to estimate
PD (ii) estimation of relative weights of PD and
Eq from empirical exercises What does it look
like in our case? Note that PD much stronger
influence on GDP than Eq, (as might have been
expected), so what determines PD? Further,
separate work by Goodhart, Hofmann and Segoviano,
Default, Credit Growth and Asset Prices, IMF
(2005/6).
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Welfare indexes of financial fragility