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This paper was originally prepared for the IMF Research Department conference on early-warning systems, November 2001. We would like to thank conference participants and many IMF staff, including Paul Cashin and Robert Rennhack for useful comments, and Manzoor Gill for superb research assistance.
Predicting currency crises is closely related to predicting exchange rate movements, so any success of EWS models is notable in the context of the long literature, starting with Meese and Rogoff (1983), that has shown how difficult it is to predict exchange rates out of sample. Berg and others (1999) discuss this point further in the EWS context.
More recently, other models have been developed in the IMF, such as Mulder, Perrelli, and Rocha (2002) and Abiad (2003). Other recent developments include models designed to predict other sorts of crises, Manasse, Roubini, and Schimmelpfennig (2003). For a review of recent developments in this literature, see Abiad (2003).
The measure of the severity of crisis for a particular country is the maximum value reached by the exchange market pressure index within 1997, where the index itself is a weighted average of the depreciation of the exchange rate and the loss of international reserves.
Although one could rationalize the low sovereign spreads in the Asian economies on the basis of their relatively low levels of external debt, spreads did increase after October 1997, suggesting that markets may have underestimated risks. The period before the Asian crises was characterized by unusually low spreads for almost all emerging market economies.
See Sy (2003) and Reinhart (2002). As with sovereign spreads, it could be argued that these ratings are designed to predict default, not currency crisis. Against this, however, is the fact that currency crises do increase the risk of default and that, because of this, ratings have in fact been downgraded after most currency crises. This suggests that the rating agencies would have likely downgraded the countries had they seen the currency crises coming.
See Economist Intelligence Unit, Currency Risk Handbook, June 2001.
The Economist Intelligence Unit forecasts are similarly unsuccessful when compared to crises as defined by the EIU itself.
The main benefit from this hindsight was the inclusion of short-term-debt/reserves as a predictive variable. The original KLR model had focused on M2/reserves instead. This latter variable also works, though not as well.
Appendix II explains how in-sample and out-of-sample periods are determined for each of the models considered.
The two private sector models monitored at that time, GS and CSFB, forecast only over a one to three month horizon. The “snapshot” of the first “official” July 1999 results is thus not informative. Their performance is examined in the discussion of overall goodness of fit, below.
Using the Economist Intelligence Unit’s own crisis definition, the forecasts perform somewhat worse, with the average risk for the crisis countries below the average for the non-crisis countries.
Excluding Pakistan, the average spread for non-crisis countries declines to 312 from 462.
The relative weights depend implicitly on the cost imputed to each type of error. Demirguc-Kunt and Detragiache (1999), who employ a similar loss-function approach in looking at banking crisis prediction, discuss the relative weights in terms of the costs of policies and regulations to increase the resilience of the banking system versus the costs of rescue of failed institutions.
We estimate this regression using OLS with HAC standard errors. This solves two sorts of problems. First, the C24 and PredProb variables are highly serially correlated, which causes the OLS standard errors to be incorrect. Monte carlo exercises suggest that in our setup, the OLS standard errors are substantial underestimates but that a HAC correction largely solves this problem. Second, the C24 variable is qualitative, resulting in a heteroskedastic ε, as is well known from the “linear probability” literature. The usual solution is to run a probit or logit regression. Here, though, the relationship between PredProb and C24 will be linear under either the null (with β = 0) or the alternative (with β = 1.) The heteroskedasticity is of known form, suggesting FGLS. However, some observations will produce negative variances. The usual solution is to apply some ad hoc adjustment to these observations, such as dropping them. Our own experience and some monte carlo exercises confirm much earlier conclusions that these procedures are unsatisfactory and suggest that OLS with HAC standard errors produces reasonable results with only a small loss of efficiency compared to GLS. (See Judge, Griffiths, Hill, and Lee (1980) on the linear probability model.) Berg and Coke (2004) discuss similar problems in the estimation of EWS models themselves. Harding and Pagan (2003) address related issues in a different context.
Each model’s own definition of crisis used to evaluate its performance. See Box 1 on crisis definitions.
This assessment is based on this paper’s metric for evaluating performance. CSFB’s own method uses a different loss function to choose a cut-off, putting more weight on missed crises. In effect, their objective is to minimize false alarms, subject to achieving a certain share of correctly called crises. Also note that CSFB uses the probabilities in a more complex way to generate various levels of risk warnings for clients, based on changes in the probabilities over the most recent one to six months. We have not evaluated how well this system does in predicting crises.
To put this problem another way, the pre-crisis observations and the predicted probabilities are highly serially correlated; adjusting for this factor greatly increases the standard errors in the model. This also implies that adding observations through extending the time dimension of the out-of-sample period is not as helpful as the increase in the number of total observations would suggest.
In addition, there are some differences in the way the out-of-sample forecasts were generated. Those for the GS model come directly from contemporary monthly publications, so they necessarily reflect incomplete data that had to be filled with estimates for various predictive variables. For example, the January 1999 crisis probability (for April 1999) uses GS estimates of data as of January 1999. The CSFB estimates, in contrast, may use revised predictive variables, although it is not clear how substantial are revisions. This issue is somewhat less serious for the DCSD and KLR models as they forecast over much longer horizons. The July 1999 forecasts, for example, made use of data available only through April for many series, but since the forecast horizon is so long, the use of such data did not make the forecasts obsolete.
The countries involved in these 46 observations of technically false, but still useful alarms are Argentina, Chile (before July 1999), Pakistan, Venezuela, Turkey, and Uruguay.
The serial correlation in the data also reduces the effective amount of information, as discussed in footnote 17. An earlier version of this paper analyzed data through end-1999 and found that the DCSD model performed as well out of sample as in sample. This dependence of the results on the sample is captured by the low power of the tests.
Detragiache and Spilimbergo (2001) and Manasse, Roubini, and Schimmelpfennig (2003) look at determinants of debt crises. Hemming, Kell, and Schimmelpfennig (2003) look at fiscal vulnerabilities in emerging market economies. Sy (2003) emphasizes that debt and currency crises are distinct events.
A more complete analysis should correct for the influence of other factors that contribute to currency crises and would consider de facto classifications such as those presented in Levi-Yeyati and Sturzenegger (2001) and Reinhart and Rogoff (2003).
Goldstein, Kaminsky, and Reinhart (2000) add some new indicators and update the KLR model. They find that the best monthly indicators for predicting currency crisis were: real exchange rate appreciation, a banking crisis, a decline in equity prices, a fall in exports, a high ratio of broad money to reserves and a recession; while the best annual indicators were a large current account deficit relative to both GDP and investment.
The model uses mainly monthly data, but also some quarterly or, for some countries, annual data. These latter series are interpolated or extrapolated to generate monthly crisis predictions.
The BIS adopts a less common approach, in that after each variable is converted to a score from a set scale, the scores are aggregated by summing using judgmental weights.
There are also a number of new approaches that are being explored in the literature. For example, Burkart and Coudert (2000) use linear discriminant analysis; Vlaar (1999) and Fratzcher (1999) develop switching regime models; and Osband and Van Rijckeghem (2000) use non-parametric methods to identify safe zones.
The Goldman-Sachs GS-Watch model also uses predictive indicators in zero/one form, but these are used as regressors in a logit model. Therefore, the probabilities are less “jumpy” than in the KLR indicators model.
Similarly, in-sample estimation periods for KLR and DCSD must end some 24 months before the time the model is being estimated, as it is not yet known whether later observations are pre-crisis or tranquil periods. For example, the in-sample period for the DCSD model in OP 186 ended in May 1995 so that the estimation did not reflect knowledge of the Asia crises that began in July 1997.