Abstract
This paper proposes a measure of exchange market pressure for countries operating in hard peg regimes, such as currency unions, currency boards or full dollarization. We use a general model of currency crisis to derive a sustainability index based upon the relationship between the shadow exchange rate and the output gap required to maintain the currency peg. We apply the index to European Union countries in order to assess the sustainability of the euro.
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Notes
A critical review of the literature on the methods used to calculate EMP can be found in Li et al. (2006). Willett et al. (2012) show how the difficulties encountered in computing EMP are related to the difficulties in classifying exchange-rate regimes. The sustainability of hard peg regimes cannot, of course, be measured using Girton-Roper type indexes since crucial variables such as international reserves, exchange rates, and interest rates are not observable at a country level.
A similar approach can also be found, e.g., in Arghyrou and Tsoukalas (2011) and De Grauwe (2011). However, they do not make use of the shadow exchange rate and also do not derive a sustainability index for currency unions that can act as a signal of vulnerability of member countries to speculative attacks and crises.
A detailed analytical discussion of these two approaches, including the basic models and their extensions centered on the relationship between financial fragility, currency crisis and the contagion across markets and countries, can be found in Piersanti (2012).
We abstract here from the real interest rate effect to focus only on the competitiveness issue which has been central to the policy debate about how to manage and correct macroeconomic imbalances within the euro area (see, e.g., Gross 2012; De Grauwe 2012). The interest effect is explored in Buiter et al. (2001), who use a more general model including an interest channel for the transmission of monetary policy to analyze the collapse of the ERM in Europe.
This is typical of the EMU, where a country willing to join the common currency is required to maintain, for an agreed time span, limited deviation from its target rate against the euro.
See, for example, Bahmani-Oskooee (1991). As the Marshall-Lerner or elasticities condition refers to a long-run analysis, the γ coefficient must be negative and statistically significant.
Analogous results were obtained using estimates of \( \left({y}_t^{i,F}-{\overline{y}}^i\right) \) from the IMF.
Results do not change if the above scale is multiplied by a factor k > 0. In order to rank countries according to their anti-inflation reputation, we used the annual percentage change in CPI over the period 1976–2010.
It is of interest to note, however, that the results (available upon request) show no visible change when θ is left to vary in the range [4.6, 3.7] for all countries.
The conversion rates are: 1.95583 for the Deutsche mark; 6.55957 for the French franc; 200.482 for the Portuguese escudo; 1,936.27 for the Italian lira; 0.787564 for the Irish pound; 340.750 for the Greek drachma; 166.386 for the Spanish peseta.
The figure was built using the transformation of the computed indicators of market pressure for each country in standard indexes with mean 0 and standard deviation 1, and applying the ± 2 standard deviation bounds as critical thresholds to identify crisis episodes in the foreign exchange market. This implies the assumption of normality in the statistical distribution of the market pressure index conforming to the literature (see, e.g., Eichengreen et al. 1995, 1996; Sachs et al. 1996; Kaminsky et al. 1998; Kaminsky and Reinhart 1999).
As shown in Canofari et al. (2014b), the different behavior of standardized EMP indexes in the two sub-periods is consistent with market’s beliefs about euro sustainability as reflected in the dynamics of spreads: in the pre-crisis period (2000–2008) markets looked at the EMU as a fully credible monetary union where the probability to exit is zero and spreads are virtually zero (i.e., investors priced the default risk in non-core countries in the same way as the risk of core countries); after the crisis (2008–2012) investors’ confidence in the irreversibility of the euro loosened, markets started looking at the EMU as a system of fixed exchange rates involving the risk of exchange rate realignments and spreads widened.
This agrees extremely well with the results obtained from the online betting platform INTRADE, which imply that markets priced the probability of a country’s exit from the euro area by the end of 2013 at 65, 40, and 60 % in November 2011, in March 2012 and in August 2012, respectively (see, e.g., Klose and Weigert 2012, Chart 19612; Shambaugh 2012, Fig. 1). It is striking to learn that these probabilities most likely reflect the probability of an exit of Greece.
Including Germany’s rate changes only marginally the results shown in Table 4, as the first component explains in this case 76 % of total variation and the first two account for over 92 % of the total. A more deeply econometric investigation aiming to test the power of our sustainability index in capturing market expectation of a euro break-up in the European sovereign debt crises is in Canofari et al. (2014b). Numerical simulation computing contagion effects across EU peripheral countries, using game theory and a similar macroeconomic set-up, can be found in Canofari et al. (2014a).
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Acknowledgments
For helpful comments and discussions, we tank Barbara Annicchiarico, Luisa Corrado, Giovanni Di Bartolomeo, Laurence Harris, Alessandro Piergallini, Pasquale Scaramozzino, Yothin Jinjarak, an anonymous referee and seminar participants at the II Workshop in International Economics, University of Rome Tor Vergata, the XX International Tor Vergata Conference on Money, Banking and Finance, the XXIV Villa Mondragone International Economic Seminar, as well as seminar participants at CeFiMS, SOAS, University of London, the University of Rome Tor Vergata and the University of Teramo.
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Appendices
Appendix A
Table 2 shows augmented Dickey-Fuller (ADF) test statistics for x i,t − m i,t and ε i,t both in levels and first differences. The sample covers quarterly data over the period 1985Q1 – 2010Q2. Lag lengths have been set equal to 5. For both variables, ADF is less than its critical value only when we use first differences. This means that the hypotheses that x i,t − m i,t and ε i,t have a unit root cannot be rejected. On the other hand, we can reject the hypotheses that first differences of x i,t − m i,t and ε i,t have a unit root.
Column 7 of Table 3 shows the vector error correction estimates displaying the long run equilibrium relationship between x i,t − m i,t and ε i,t : Standard errors and T-statistics are shown in brackets. Table 3 shows that we cannot reject the hypothesis that both x i,t − m i,t and ε i,t are cointegrated for each country and that the cointegrating relationships are stable throughout the sample period.
Appendix B
Data Sources
- y t :
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Real GDP (billions of national currency). Source: IMF: World Economic Outlook. Period: 1999–2012.
- ε i,t :
-
Real effective exchange rate. Source: IMF: International Financial Statistics. Period: 1980Q1 - 2010Q3.
- x t :
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Goods exports (millions of US Dollars). Source: IMF: International Financial Statistics. Period: 1980Q1 - 2010Q3.
- m t :
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Goods imports (millions of US Dollars). Source: IMF: International Financial Statistics. Period: 1980Q1 - 2010Q3.
- CPI :
-
Consumer Price Index. Source: OECD: Main Economic Indicators. Period: 1976–2010.
Dynamics of EMP index under alternative values for θ
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Canofari, P., Marini, G. & Piersanti, G. Measuring Currency Pressure and Contagion Risks in Countries under Monetary Unions: The Case of Euro. Atl Econ J 42, 455–469 (2014). https://doi.org/10.1007/s11293-014-9434-2
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DOI: https://doi.org/10.1007/s11293-014-9434-2