1 / 23

Contagion Effects in Currency Crises: Analyzing the 1998 Russian Crisis and Beyond

This dissertation examines the contagion effects stemming from the Russian currency crisis of August 1998, focusing on macroeconomic similarities between various Eastern European countries including Ukraine, Latvia, and Poland. It explores whether these similarities facilitated the spread of the crisis and identifies key domestic economic fundamentals that influenced exchange market pressures. The study employs a range of models, including Krugman’s and Obstfeld’s frameworks, to analyze how economic variables like domestic credit and current account influence contagion dynamics.

nasya
Télécharger la présentation

Contagion Effects in Currency Crises: Analyzing the 1998 Russian Crisis and Beyond

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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).

More Related