Integration of Center and Eastern European Stock Markets MSc student IOSIF ANAIDA Coordinator Professor Moisă Altăr The Academy of Economic Studies Doctoral School of Finance and Banking Bucharest, July 2007

Dissertation paper outline   The integration of the emerging stock markets   The aims of the paper   Empirical studies concerning stock markets integration   The Data   Testing the cointegration   Testing the correlation   Conclusions   References

Different approaches  Bekaert and Harvey (1997) – market liberalization increase the correlation between local market returns and the world market but do not drive up local volatility.  Forbs and Rigobon (1999) – there was no contagion during the euro- Asia crises in 1997, the Mexican peso collapse in 1994 and in the US stock market crash in High market co-movements during these periods where a continuation of strong cross-market linkages  Egert and Kocenda (2005) – there are no robust cointegration relationship between emerging and developed markets. But there are short-term spillover effects in terms of stock returns and stock price volatility.  Cappielo, G’erard, Kadareja and Manganelli (2006) – larger new EU member state exhibit a strong comovements between themselves and with the euro area. Form the smaller countries only Estonia and Cyprus show integration bough with the euro zone and the block of large economies.

The Data  Initial data series: Bucharest Exchange Trading index (BET), Prague Exchange index (PX), Warsaw Exchange index (WIG20), Bulgarian Exchange index (SOFIX), Budapest Stock Index (BUX) Austrian Traded Index (ATX).  Time length: –

The Cointegration analses The Cointegration analyses   Verify stationarity of series using ADT, Phillips-Perron and KPSS tests: the series are not stationary in level but is stationary in first difference   Check the cointegration relationship between the variables using the Engle Granger residual based cointegration method:   Estimate residuals series for each regression.   Verify the stationarity of the residual series using ADF and PP tests.   Comparing the test statistic with the critical values estimated by Engle and Yoo. Engle-Granger cointegration test Variables ADF PP ATX * * BET * * BUX * * PX * WIG * * SOFIX * *

Cointegration analysis   Johansen method in a VAR framework   Select numbers of lags to include in the VAR using the Akaike informational criterion   Check VAR stability Cointegration - conclusions   The residuals are not stationary, the value of t statistic is higher then the critical value   The Johansen method – the test statistic is smaller then the critical values   There is no cointegration relationship between the series.

The correlation analysis for the returns  Calculating the returns: dl_index Correlation matrix for returns

The correlation analysis  Choosing the order of the variables using the F-test, market capitalization and the efficiency of the market: ATX, WIG, PX, BUX, BET, SOFIX.  Verify the sign and proportion of the spillover between the returns using the impulse response and variance decomposition.  Lag length criteria suggests a specification including 1 lag  Verify the short-term interaction between returns using Granger causality test:

The response of returns to shocks applied on the other markets

Variance decomposition for the returns  The initial shock in the returns works through the system in about 3 days  None of the emergent markets influence the Austrian returns, but changes in returns on the three larger emergent stock markets are due to changes in the Austrian returns  The three larger emergent markets: Poland, Czech Republic and Hungary are correlated between themselves in terms of returns  BET and SOFIX returns seem uninfluenced by the movements of the other returns.

Methods in obtaining returns volatility  Obtained variances series for returns using GARCH(1,1) method - the mean equation: - the mean equation: - the conditional variance equation:  Using a EGARCH(1,1,1) method to estimate variance for SOFIX returns  Conditional variance equation for the EGARCH:  Advantages in using a EGARCH method: - the coefficients can be negative because - the coefficients can be negative because is modeled. - the asymmetry of the EGARCH model capture the leverage effect.

Volatility - the correlation analyze  Verify stationarity of the variance using the ADF and PP tests, the series are stationary at any significance level.  Check the relation between the series using the matrix correlation and Granger Causality test.

Impulse response for volatility

Variance Decomposition for volatility  The initial shock in the volatilities works through the system in about 90 days, exception being WIG volatility which affects the market for about 5 months.  Changes in ATX volatility have a positive influence on Poland, Czech Republic and Hungarian volatility.  The three larger emergent markets are correlated in terms of volatilities between themselves.  Romanian and Bulgarian volatilities are correlated with volatilities in Poland and Hungary.

Conclusions  There are no cointegration relationships between the countries under study  None of the emerging markets has a significant influence on the industrialized market  There are unidirectional spillovers from Austria to Poland, Hungary and the Czech Republic in term of returns and volatility.  Between the larger emerging markets there are correlations relationships in returns and volatility.  Romania and Bulgarian stock markets are driven mainly by the developments at domestic level.  Spillover effects between volatilities are stronger compared to spillover effects between returns.

References 1) Angeloni, I., M. Flad, and F. P. Mongelli (2005), “Economic and Monetary Integration of the New Member State. Helping to Chart the Route”, European Central Bank, Occasional Paper Series, no.36 2) Bekaert, G. and C.R., Harvey (1997), “Emerging Equity market volatility”, Journal of Financial Economics 43 3) Bekaert,G., C.R. Harvey and A. Ng (2003), “Marketing Integration And Contagion”, NBER Working Paper no ) Brooks, C (2002), “Introductory Econometrics for Finance”, Cambridge University Press 5) Cappiello, L., B. Gerard, A. Kadareja and S. Manganelli (2005), “Equity Market Integration of New EU Member States”, (2006), “Financial Integration of New EU Mem- ber States”, European Central Bank, Working Paper Series no ) Cerny, A., (2004), “Stock Market Integration and the Speed of Information Transmission”, Charles University, Center for Economic Research and Graduate Education, Academy of Sciences of the Czech Republic, Economic Institute, Working Paper Series 242 7) Cheung, Y.-L., Y.-W. Cheung and C.C. Ng (2006), “East Asian equity markets, financial crises, and the Japanese currency”, Journal of The Japanese International Economies, 21, ) Dabla-Norris, E. and H. Floerkemeier (2006), “Transmission Mechanisms of Monetary Policy in Armenia: Evidence from VAR Analysis”, IMF Working Paper, 06/248 9) Danthine, J.-P., F. Giavazzi and E.L. Von Thadden (2000), “European Financial Markets After EMU: A First Assessment”, NBER Working Paper no ) Egert, B. and E. Kocenda (2005), “Contagion Across and Integration of Central and Eastern European Stock Markets: Evidence from Intraday Data”, William Davidson Institute Working Paper, no. 798

11) Engle, R.F. and C.W.J. Granger (1987), “Co-Integration and Error Correction: Representation, Estimation, and Testing”, Econometrica, 55, pp ) Engle, R.F. and H. White (1999), “Cointegration, Causality, and Forecasting”, Oxford University Press 13) Forbes, K. and R. Rigobon (1999), “No Contagion, Only Interdependence Measuring Stock Market Co- Movements”, NBER Working Paper no.7267 (2001), “Contagion in Latin America: Definitions, Measurement, and Policy Implications” 14) Gujarati, D.N., (1995), “Basic Econometrics”, McGraw-Hill, Inc 15) Hajalmarsson, E. and P. Osterholm (2007), “Testing for Cointegration Using the Johansen Methodology when Variables are Near-Integrated”, IMF Working Paper, 07/141 16) Hall, S.G., G. Hondroyiannis (2006), “Measuring the Correlation of Stocks between the EU-15 and the New Member Countries”, Bank of Greece, Working Paper no.31 17) Harris, R.I.D. (1995), “Using Cointegration Analysis in Econometric Modeling”, Prentice Hall 18) Janakiramanan, S. and A.S. Lamba (1998), “An empirical examination of linkages between Pacific- Basin stock markets”, Journal of International Financial Markets, Institutions and Money, 8, ) Miyakoshi, T. (2002), “Spillovers of stock return volatility to Asian equity markets from Japan and the US”, Journal of International Financial Markets, Institutions and Money, 13, ) Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, no. 2 21) Nicolo, de G. and A. Tieman (2006), “Economic Integration and Financial Stability: A European Perspective”, IMF Working Paper, 06/296 22) Obstfeld, M. and A.M. Taylor (2002), “Globalization and Capital Markets”, NBER Working Paper no.8846

23) Phylaktis, K., (1999), “Capital market integration in the Pacific Basin region: an impulse response analysis”, Journal of International Money and Finance, 18, ) Rockinger, M. and G. Urga (2000), “A Time Varying Parameter Model To Test For Predictability And Integration In Stock Markets Of Transition Economies”, CEPR Discussion Paper no ) Syllignakis, M. (2006), “EMU`s Impact on the Correlation across the European Stock Markets”, International Research Journal of Finance and Economics 26) Terasvirta, T. (2006), “An Introduction to Univariate GARCH Models”, SSE/EFI Working Papers in Economics and Finance, No ) 28) 29) 30) 31) 32)

Engle Granger residual base cointegration test

Estimating the volatilities