Contagious Currency Crises

1. The Academy of Economic Studies Doctoral School of Banking and Finance Contagious Currency Crises - Dissertation Paper- Student: Dumitru Delia Supervisor: Prof. Moisã Altãr Bucharest, July 2003

2. Objectives: • The Currency Crisis from Russia, august 1998: testing for the existence of a contagion effect; • Determine whether the macroeconomic similarities between countries represented a channel of contagion; • Determine the domestic economic fundamentals that influenced the pressure on the exchange market.

3. Definitions: • A currency crisis is usually defined as a situation in which an attack on the currency leads to a sharp depreciation of the exchange rate. • Testing for contagion means searching whether the probability of a crisis in a country at a point in time increases the probability of crises in other countries after controlling for the effect of political and economic fundamentals.

4. Krugman’s Model (1979) - crises were caused by weak economic fundamentals; Obstfeld’s Model (1986)- self-fulfilling crises; Early Warning System Models: -Kaminsky, Lizondo and Reinhart, 1998; -Eichengreen, Rose and Wyplosz, 1996; Gerlach and Smets (1995)- trade links; Goldfajn and Valdes (1995) – illiquidity; Eichengreen, Rose and Wyplosz (1996)- trade and similarity links; Sachs, Tornell and Velasco (1996)- contagion due to similar economic features. Litherature Review Three generations of models referring to currency crises: Contagious Currency Crises

5. The Data: • Countries: Russia, Ukraine, Latvia, Lithuania, Estonia, Poland, Hungary, the Czech Republic, the Slovak Republic, Romania and Bulgaria; • Quarterly Data: Q1:1993- Q1:2003; • Date Sources: International Financial Statistics, IMF-World Bank-OECD-BIS joint table.

6. When did speculative attacks take place? • Index of exchange market pressure: where: ei,t - the price of a USD in country’s i currency at time t; Δii,t - the variation of short term interest rate; Δri,t - the variation of international reserves; α, β, γ - weights.

7. When did speculative attacks take place? • Extreme values of EMP: 1, if EMPi,t≥1.5σEMP+μEMP Crisisi,t= 0, otherwise. • Results:

8. The Model • Equation: • Fundamentals: - domestic credit; - current account; - CPI growth; - employment; - GDP growth; - unemployment; - money; - government deficit; - ratio of short term debt to reserves; - deviation of the real exchange rate from the trend.

9. The Model • Determine the macroeconomic similarities whose existence might be a potential channel for contagion. • Being “similar” means having similar macroeconomic conditions; • Similarity weights: • Variables: domestic credit, money, CPI, output growth and current account.

10. The Czech Republic EMP index • Russia EMP- significant positive coefficient; • Current account similarity: significance (1%); domestic credit and money-no sign. • Domestic influences: • - domestic credit(+); • - ratio of short term debt to reserves(+); • - percentage of current account in GDP(-); • - economic growth(-). • R-squared 0.628581 • Adjusted R-squared 0.559800 • S.E. of regression 0.042134 • Schwarz criterion-3.060881 • Akaike info criterion-3.332973

11. Bulgaria • The probability that Russia EMP might be significant is around 50%; • Domestic fundamentals found significant: • - CPI inflation(+); • - current account(-); • - ratio of short term debt to reserves(+); • - deviation of real exchange rate from trend(+). EMP Index • R-squared 0.836911 • Adjusted R-squared 0.816524 • S.E. of regression 0.192737 • Schwarz criterion-0.112205 • Akaike info criterion-0.329896

12. Estonia • Russia EMP - significant positive coefficient(1%); • GDP similarity: best results; • Significant influence: • - domestic credit(+); • - percentage of current account in GDP(-); • - CPI inflation(+). EMP Index • R-squared 0.443104 • Adjusted R-squared 0.339975 • S.E. of regression 0.040774 • Schwarz criterion-3.126489 • Akaike info criterion-3.398581 Breusch-Godfrey Serial Correlation LM Test: F-statistic0.64710Prob0.532106 Obs*R-squared0.377274 Prob0.828087

13. Latvia Similarity weights: • No evidence of contagion(35%); • Significant influences: • Election(+); • Current account(+); • - CPI inflation(+). • R-squared 0.576670 • Adjusted R-squared 0.513954 • S.E. of regression 0.017422 • Schwarz criterion-4.890526 • Akaike info criterion-5.119547 • Durbin – Watson stat 2.082432

14. Lithuania EMP Index • No evidence of contagion; • High current account similarity; • Significant influence: • - domestic credit(+); • - money(+); • - deviation of real exchange rate from trend(+). • R-squared 0.618261 • Adjusted R-squared 0.578770 • S.E. of regression 0.020152 • Schwarz criterion-4.676371 • Akaike info criterion-4.857766 • Durbin – Watson stat 2.071606

15. Poland EMP Index • EMP Russia – significant; • GDP similarity - best results; • Significant influences: • - government deficit(-); • - domestic credit(+); • - deviation of real exchange rate from trend(+). • R-squared 0.742046 • Adjusted R-squared 0.677558 • S.E. of regression 0.028311 • Schwarz criterion-3.801626 • F-statistic11.50665 • Prob(F-statistic) 0.000025 • Akaike info criterion-4.091956

16. The Slovak Republic • EMP Russia – positive coefficient; • High current account similarity; • Influences: • - GDP growth(-) • - money(+) • - deviation of real exchange rate from trend(+) • - domestic credit(+) • - ratio of short term debt to reserves(+) EMP Index • R-squared 0.777728 • Adjusted R-squared 0.728334 • S.E. of regression 0.023180 • Schwarz criterion-4.195540 • Akaike info criterion-4.091956

17. Ukraine • EMP Russia significant; • All similarity coefficients are high; • Significant influences: • - money(+); • - current account(-). EMP Indexes • R-squared 0. 854556 • Adjusted R-squared 0.806074 • S.E. of regression 0.077818 • Schwarz criterion-1.735484 • Akaike info criterion-2.033919 • Durbin-Watson 1.783723

18. Hungary • No evidence of contagion; • Significant influence: • -CPI inflation(+); • - deviation of real exchange rate from trend(+); • - domestic credit(+); • - employment(-); • - money(+); • - current account(-). EMP Index • R-squared 0.829776 • Adjusted R-squared 0.793300 • S.E. of regression 0.026885 • Schwarz criterion-3.906587 • Akaike info criterion-4.217656

19. Romania EMP Index • EMP Russia – positive significant coefficient; • Domestic fundamentals: • - CPI inflation(+) • - deviation of real exchange rate from trend(+) • - ratio of short term debt to reserves(+) • - Government deficit(+)

20. Romania • Variable CoefficientStd. Errort-StatisticProb. • D(CPI,2) 0.000835 0.0004052.0620370.0518 • C 1.029751 0.2482854.1474570.0005 • D(DEF)-1.09E-05 3.83E-06-2.8403050.0098 • DGDP-1.097253 0.249253-4.4021710.0002 • D(DTSREZ) 0.778237 0.1816454.2843910.0003 • D(DEVREER,2) 0.000529 0.0001055.0340970.0001 • EMP1RUS(-3) 0.248825 0.0526444.7266020.0001 • R-squared 0.907751 Mean dependent var -0.032190 • Adjusted R-squared 0.877002 S.D. dependent var 0.158816 • S.E. of regression 0.055698 Akaike info criterion -2.708777 • Sum squared resid 0.065149 Schwarz criterion -2.331592 • Log likelihood 47.27727 F-statistic 29.52075 • Durbin-Watson stat 1.843044 Prob(F-statistic) 0.000000 Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.166708 Probability 0.917425 Obs*R-squared0.000000 Probability 1.000000

21. Romania • Bilateral trade weights: twice the percentage of exports and once the percentage of imports with Russia; • The Wald test in this case: F-statistic 80.62561Probability0.000000 Chi-square80.62561Probability 0.000000

22. Conclusions • A speculative attack in Russia seems to have increased significantly the odds of an attack in 6 of the countries included in the sample - it does not represent a definitive proof of contagion; • The hypothesis that attacks spread to other countries where economic policies and conditions are similar is not always confirmed – similarities are difficult to capture in a weighting scheme. • The fundamental causes of speculative attacks differ across countries- it is very difficult to find a set of fundamentals underlying all crises.

23. References • Abiad, A (2003), “Early Warning Systems: a Survey and a Regime – Switching Approach”, IMF Working Paper No.32/2003 ((Washington: International Monetary Fund). • Berger, W. and H. Wagner (2002), “Spreading Currecncy Crises: The Role of Economic Interdependence”, IMF Working Paper No.02/144 (Washington: International Monetary Fund). • Bussiere, M and M.Fratzcher (2002), “Towards a New Early Warning System of Financial Crises”, ECB Working Paper No. 145/2002 (European Central Bank). • Bussiere, M. and C. Mulder (1999), “External Vulnerability in Emerging market economies: How High Liquidity can offset Weak Fundamentals and the Effects of Contagion”, IMF Working Paper No.99/88 (Washington: International Monetary Fund). • Eichengreen, B., A.K.Rose and C.Wyplosz (1996), “Contagious Currency Crises”, NBER Working Paper No.5681 (Cambridge: National Bureau of Economic Research). • Frankel, J. and A.K.Rose (1996), “Currency Crashes in Emerging Markets: Empirical Indicators”, NBER Working Paper No.5437/96 (Cambridge: National Bureau of Economic Research). • Fratzcher, M. (2002), “On Currency Crises and Contagion”, ECB Working Paper No. 139/2002 (European Central Bank). • Ghosh, S. and A. Ghosh (2002), “Structural Vulnerabilities and Currency Crises”, IMF Working Paper No.02/9 (Washington: International Monetary Fund). • Kaminsky, G., S. Lizondo and C.Reinhart (1998), “Leading Indicators of Currency Crises”, Staff Papers, International Monetary Fund, Vol.45. • Kaminsky, G. and C.Reinhart (1996), “The Twin Crises: The Causes of Banking and Balance of Payments Problems”, International Finance Discussion Paper, (Washington: Board of Governors of the Federal System). • Kaminsky, G (1999), “Currency and banking Crises: The Early Warnings of Distress”, IMF Working Paper No.99/178 (Washington: International Monetary Fund). • Mathieson, D, J. A.Chan-Lau and J.Y.Yoo, 2002, “Extreme Contagion in Equity Markets”, IMF Working Paper No.02/98 (Washington: International Monetary Fund).