Exchange Rate Economics: Theories and Evidence

334 Speculative attack models and contagion
crises but only 3 banking crises. In contrast,in the 1980s and 1990s the number of
banking crises quadruples while the number of balance of payments crises hardly
changes.
Using a probit model in which a binary measure (i.e. a 0/1 measure,where
the 1 denotes a crisis and is determined by the exchange market pressure variable)
of balance of payments crisis is regressed onto their index of banking crisis they
find that a banking crisis in a country is a significant determinant of a balance of
payments crisis (the converse is not true). Kaminsky and Reinhart (1996) then go
on to analyse the evolution of nine macroeconomic variables,18 months before
and after the crises,relative to their behaviour in tranquil times. In sum,they find
that balance of payments crises are preceded by recesssions,or below ‘normal’
economic growth. Monetary policy,reacting perhaps to the recessionary forces,
is more expansionary about 6 months before the crises. Finally,the unbacked
liabilities of the financial system climb steeply just before the crises.
Kaminsky et al. (1997) use 15 monthly indicators to provide an alternative
measure of early warning. In particular,when an indicator exceeds a certain
threshold value this is interpreted as a warning signal that a currency crisis may
take place within the following 24 months. The effectiveness of different indicators
is determined on the basis of the following matrix:
Signal performance matrix
Crisis (within 24 months)
Non-crisis (within 24 months)
Signal A B
No Signal C D
Source: Kaminsky et al. (1997).
where A is number of months in which an indicator issued a good signal,B is the
number of months in which an indicator issued a bad signal or simply ‘noise’,C
is the number of months in which an indicator failed to issue (which would have
been a good signal) and D is the number of months in which indicator did not issue
a signal (a bad signal). In this context,a perfect indicator would have been one for
which A and D > 0,B and C = 0. That is,it would issue a signal in every month
that is to be followed by a crisis (A > 0 and C = 0) and it would not issue a signal
in every month that is not followed by a crisis (B = 0 and D > 0). The optimal
threshold for each indicator is then the value which minimises the so-called noise to
signal ratio: [B/B + D] / [A/A + C] which is the ratio between the false signal as a
proportion of the number of months in which false signals could have been issued,
and good signals as a proportion of all possible good signals that could have been
issued. Ceteris paribus,the lower is this ratio the better is the indicator. In particular,
a signalling device which issues signals randomly would have a ratio equal to
one and therefore Kaminsky et al. (1997) exclude indicators with a ratio of one
and above.