1. Student: Nechita Laura Coordinator:Professor Moisă Altăr A comparison of the exchange rate volatility between Central-Eastern European Currencies and Euro

2. Objectives  To approach the volatility of CEE countries (Czech, Hungary, Poland, Slovakia and Romania) exchange rates from the perspective of the permanent and transitory dimensions using Component GARCH model  To explore the question regarding the convergence between CEE economies and Euro area by the comparison of long-run volatility trends in CEE currencies and the Euro

3. Literature review  The studies on exchange rate volatility in major currencies often have used conditional variance measures of volatility and have focused on the analysis of long-run trends in exchange rate volatility.  Pramor and Tamirisa (2006) - a lower degree of commonality within CEE area, which is less than what Black and McMillan(2004) found for major industrial countries in Europe before the introduction of the euro  Kobor and Szekely (2004) - research on a sample of four countries ( Poland, Hungary, Czech and Slovak) during a period of three years (2001-2003), revealing that volatilities were highly variable from one year to another  Horvath (2005) pointed out that excessive exchange rate volatility triggers macroeconomic instability, being perceived as a bad signal by investors  Fidrmuc and Korhonen (2006) reviewed the literature on business cycle correlation between the euro area suggesting that several new Member States have already achieved a comparably high degree of synchronization with the euro area business cycle

4. Literature review  Beveridge and Nelson (1981) showed that the permanent component is a random walk with drift and the transitory component is a stationary process  Engle and Lee (1993) applied that decomposition on US and Japanese stock indices developing a statistical component model (CGARCH) in order to investigate the long- run and the short-run movement of volatility in the stock market

5. Data  EUR-CZK, EUR-HUF, EUR-PLN, EUR-RON, EUR-USD  the period January 1999 – June 2009 (except SKK – only the period January 1999-December 2008) with the following sub periods:  the full period: January 1999 – June 2009  the late period: January 2004 – December 2008  the last semester: January 2009 – June 2009

6. Data

7.  All series present unit root  log-differences:  ADF Test & PP Test– the absence of the unit root of the log- differences

8. Model C-GARCH Conditional variance of the model GARCH(1,1):

9. Model C-GARCH  Replacing with a time-varying trend  Long run component:  Transitory component:  Constrains :

10. CGARCH Estimates  Jan99-Dec08  coefficients corresponding to the long-run component are significant at level 1% and higher than the ones associated with the transitory component  the AR coefficient of permanent volatility (ρ) is highly significant (almost 1) and its size exceeds the coefficients of the transitory component => model is stable and long run component tends to be a random walk with drift  PLN and SKK present shocks mostly of transitory nature (the coefficients almost 1)  RON – especially long nature (forrecast error is positive and significant)

11. CGARCH Estimates

12.  Jan04-Dec08  stability of the model is given by the AR coefficient which is almost 1  CZK and EUR have a negative short term component (α + β inferior to 1), confirming the long term nature of shocks  The assymetric term is negative and significant (especially for Czehia, Hungary, Poland and Slovakia ), suggesting higher volatility in case of currency depreciation

13. CGARCH Estimates CZK EUR

14.  Last Semester  The currencies strongly depreciated – the asymmetric term is negative  HUF and EUR have a negative forecast error => suggesting a lower shock impact on the permanent component of the volatility  The model still confirms to be stable CGARCH Estimates

15. Permanent vs. transitory component Hodrick Prescott Filter

16. Permanent vs. transitory component

17. Principal Components Analysis - Long run component – jan99-dec08 Cattell criterion

18. Pairwise Covariance Matrix - Long run component –jan99-dec08  the weights on the first component are similar in sigh and absolute value => the common trend for the currencies CZK, PLN and EUR  the covariance matrix underlines also the same couples : CZK, PLN and EUR  the same conclusion Fidrmuc&Korhonen(2006) si Kobor&Szekely(2004)  The model still confirms to be stable

19. Principal Components Analysis - Long run component – jan04-dec08

20. Pairwise Covariance Matrix - Long run component –jan04-dec08  both methods confirm the same trend for the currencies: CZK- HUF- PLN – EUR  the same correlation was found by: Horvath(2007): CZK-PLN Kobor&Szekely(2006): PLN-HUF

21. Principal Components Analysis - Long run component – jan09-jun09

22. Pairwise Covariance Matrix - Long run component –jan09-jun09  Estimated coefficients from CGARCH were less significant than in the previous sub periods => twisted results: a strong correlation between CZK, PLN and RON  EUR-HUF correlation (78%) –beginning with 2008, Hungary adopted a free floating regime regarding the exchange rate policy

23. Principal Components Analysis: Transitory component –jan04-dec08  Euro has a common trend with SKK in the last period as it has been shown also by Pramor & Tamirisa findings (2006)

24. Principal Components Analysis: Transitory component –jan09-jun09  For the last semester, we can not conclude about the common trend between the currencies based on the transitory component

25.  Permanent component coefficients were positive and higher than the ones corresponding to the transitory component, reflecting the fact that permanent volatility component is stronger than the short term one  The dispersion and overall variability of weights for the short-run component are significantly higher than for the long-run component – not surprisingas the short- run component of volatility reflects transitory and unsystematic disturbances  The most volatility components (both for the permanent component and transitory one) belong to Romania while the lowest one to Slovakia.  For the full period, the weights of the first component revealed that Czech koruna, Polish Zloty and Euro have similar long term volatility component  SKK seemed to have a common trend with euro in the last period  Romanian currency is slowly correlated not only with the other CEE currencies but also with euro. Conclusions

26. References  Beveridge, S. and C. R. Nelson (1981), “A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the ‘Business Cycle’, Journal of Monetary Economics, Vol. 7, 151–74.  Black, Angela J. and D.G.McMillan (2004), “Long-Run Trends and Volatility Spillovers in Daily Exchange Rates”, Applied Financial Economics, Vol.14, 895-907  Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, Vol. 31, 307–27.  Engle, R.F. and G.G.J Lee (1993), “A Permanent and Transitory Component Model of Stock Return Volatility”, Discussion Paper 92-44R, University of California, San Diego  Engle, Robert F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates for the Variance of United Kingdom Inflation,” Econometrica, Vol. 50, No. 4, 987–1008.  Fidrmuc, J. and I. Korhonen (2006), “Meta-analysis of Business Cycle Correlation between the Euro Area and the CEECs,” Journal of Comparative Economics, 34, 518–537  Fidrmuc, J. and R. Horvath (2007), “Volatility of Exchanges Rates in Selected New EU Members: Evidence from Daily Data”, CESifo Working Paper No.2107, 10/2007  Horvath, R. (2005), “Exchange Rate Variability, Pressures and Optimum Currency Area Criteria: Implications for the Central and Eastern European Countries,” CNB Working Paper No. 8 (Czech Republic: Czech National Bank).  Kóbor, A. and I. P. Székely (2004), “Foreign Exchange Market Volatility in EU Accession Countries in the Run-Up to Euro Adoption: Weathering Uncharted Waters,” Economic Systems, 28(4), 337–352  Mundell, R. (1961), “A Theory of Optimum Currency Areas,” American Economic Review, Vol. 51, 657– 65.  Pramor, M. and N.T. Tamirisa (2006), “Common Volatility Trends in the Central and Eastern European Currencies and the Euro”, IMF Working Paper, 06/2006  Schnabl, G. (2007), “Exchange rate Volatility and Growth in Small Open Economies at the EMU Periphery”, ECB Working Paper No. 773, 07/2007