CURRENCY CRISES POP IULIA

Introduction  Importance and objectives of currency crises models  Empirical results: an effective warning system should consider a broad variety of indicators

SOURCES OF SPECULATIVE ATTACKS  The First-Generation Models - Latin American crises in the 1980s  Krugman (1979) and Flood and Garber (1984)  The approach: inconsistency between a fixed exchange rate rule and the pursuit of domestic policies such as monetising large fiscal and current account deficits  The sudden speculative attacks on currencies are rational

SOURCES OF SPECULATIVE ATTACKS  The Second-Generation Models - European Exchange Rate Mechanism crises  Obstfeld (1994,1996)  The approach: dynamic interactions of market expectations and the conflicting objectives of the government => self-fulfilling run on the domestic currency  Multiple equilibria and herding behavior by investors

How do these models perform empirically?  The empirical literature has not been able to determine whether first or second-generation models are better explaining currency crashes  Indicators such as the real exchange rate, foreign reserves and a weak banking system are useful in predicting crises

SOURCES OF SPECULATIVE ATTACKS  The Third-Generation Models: Asian Crisis  Eichengreen, Rose and Wyplosz (1995), Sachs, Tornell and Velasco (1996), Kaminsky, Lizondo and Reinhart (1997, 1998)  Focus on a broader set of fundamentals: political, institutional, financial variables  What indicators worked best: international reservesinternational reserves real exchange ratereal exchange rate credit growthcredit growth export performanceexport performance domestic inflationdomestic inflation

THE NONPARAMETRICAL “SIGNALS” APPROACH  It involves monitoring the evolution of a number of economic variables; when one of these variables deviates from its “normal” level beyond a “threshold”, this is taken as a warning signal about a possible currency crisis within a specified period of time  The definition of a currency crisis: a situation in which an attack on the currency leads to a sharp depreciation of the currency, a large decline in international reserves, or a combination of the two.

THE METHOD  The countries: HungaryAustria RomaniaGermany PolandGreece Slovak RepublicItaly TurkeySpain Russian Federation  The sample length:  The signaling horizon: 24 months

THE EXCHANGE MARKET PRESSURE INDEX For each country, crisis is identified ex post by the behavior of an index: where T (threshold level) is set at three standard deviations from the mean where = monthly percentage changes in the exchange rate where = monthly percentage changes in the international reserves The weights are chosen so that the two components of the index have the same variance.

THE EXCHANGE MARKET PRESSURE INDEX - results -   CountryNumber of Crises Period   Hungary 0 -   Romania 2January 1997 February 1997   Poland 1 August 1996   Slovakia 2 March 1999   May 1999   Turkey 1 January 1997   Russia 2August 1998   September 1998   Austria 0-   Germany 0-   Greece 0-   Italy 1 February 1998   Spain 0-

THE EXCHANGE MARKET PRESSURE INDEX Romania The results are consistent with the IMF Country Report 01/16 for Romania. The evolution of the index does not reflect the 1998 contagion effects of the Russian financial crisis and the 1999 pessimistic expectations of the market.

THE INDICATORS The choice of indicators was dictated by theoretical considerations and by the availability of information on a monthly data. They are:  exports (in U.S. dollars)  imports (in U.S. dollars)  monetary aggregate M1  real exchange rate  gross international reserves For all these variables the indicator on a given month was defined as the percentage change in the level of the variable with respect to its level a year earlier => the comparability of units across countries, it eliminates the seasonality effects

DEFINITIONS  Signaling horizon = the period within which the indicator would be expected to have an ability for anticipating crises, and is defined a priori as 24 months.  Signals: an indicator is said to issue a signal whenever it departs from its mean beyond a given threshold level.  Thresholds: are defined in relation to percentiles of the distribution of observations for each indicator. While the percentile used as reference (20%) is uniform across countries, the corresponding indicator-specific thresholds are different.

DEFINITIONS The performance of each indicator is examined in terms of the following matrix: Crisis No crisis (within 24 months) A Signal was issued A B D No signal was issued C D A = no. of months in which the indicator issued a “good” signal B = no. of months in which the indicator issued a “bad” signal or “noise” C = no. of months in which the indicator failed to issue a signal D = no. of months in which the indicator refrained from issuing a signal

EMPIRICAL RESULTS - information on the performance of individual indicators is presented in the next table - Final Results: - tendency of indicator to issue good signals: RER RER Imports Imports Exports Exports Reserve Reserve - tendency of indicators to issue bad signals: Imports Imports Exports Exports - noise/signal ratio: Reserves Reserves RER RER

ROMANIA - EMPIRICAL RESULTS 1. The outcome of the analysis is consistent with the theoretical and empirical currency crises literature 2. The real exchange rate and international reserves are the indicators with the highest power of anticipating a crisis situation on the foreign exchange market