integration of center and eastern european stock markets msc student iosif anaida coordinator...

Integration of Center and Integration of Center and Eastern European Stock Eastern European Stock

MarketsMarkets

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

DifferentDifferent approachesapproaches

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 1987. 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 DataThe Data

0

1000

2000

3000

4000

5000

2001 2002 2003 2004 2005 2006

ATX

4000

8000

12000

16000

20000

24000

28000

2001 2002 2003 2004 2005 2006

BUX

200

400

600

800

1000

1200

1400

1600

1800

2001 2002 2003 2004 2005 2006

PX

0

200

400

600

800

1000

1200

1400

1600

2001 2002 2003 2004 2005 2006

SOFIX

800

1200

1600

2000

2400

2800

3200

3600

2001 2002 2003 2004 2005 2006

WIG

0

2000

4000

6000

8000

10000

2001 2002 2003 2004 2005 2006

BET

Initial data series: Initial data series:

Bucharest Exchange Bucharest Exchange Trading index (BETTrading index (BET), ),

Prague Exchange index Prague Exchange index (PX), (PX),

Warsaw Exchange index Warsaw Exchange index (WIG20),(WIG20),

Bulgarian Exchange index Bulgarian Exchange index (SOFIX),(SOFIX),

Budapest Stock Index Budapest Stock Index (BUX)(BUX)

Austrian Traded Index Austrian Traded Index (ATX). (ATX).

Time length: Time length: 20.10.2000 – 04.03.200720.10.2000 – 04.03.2007

The Cointegration analThe Cointegration analysesses

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 -3.420593* -3.509189*

BET -3.648032* -3.380750*

BUX -4.589758* -4.270524*

PX -5.088.152 -4.560678*

WIG -3.674414* -3.486132*

SOFIX -2.972781* -3.215374*

n

i ttiit YX1 ,1

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

tktkttktt yyyyy )1(12211 ...

Cointegration - conclusions

The residuals are not stationary, the value of t statistic is higher then the critical value The Johansen method – the teststatistic is smaller then the critical values There is no cointegration relationship between the series.

No. of cointegration

Eigenvalue Test statistic 5% critical value

1% critical value

None 0.020503 29.99762 39.37 45.10At most 1 0.011279 16.42501 33.46 38.77At most 2 0.010257 14.92850 27.07 32.24At most 3 0.007897 11.48052 20.97 25.52At most 4 0.004609 6.688568 14.07 18.63At most 5 3.56E-05 0.051499 3.76 6.65

Johansen cointegration test-eigenvalue max

No.of cointegration

Eigenvalue Test statistic 5% critical value

1% critical value

None 0.020503 79.57171 94.15 103.18At most 1 0.011279 49.57410 68.52 76.07At most 2 0.010257 33.14909 47.21 54.46At most 3 0.007897 18.22059 29.68 35.65At most 4 0.004609 6.740067 15.41 20.04At most 5 3.56E-05 0.051499 3.76 6.65

Johansen cointegration test-eigenvalue trace

The correlation analysis for the returns

Calculating the returns: dl_indexCalculating the returns: dl_index

ATX BET BUX PX SOFIX WIGATX 1.000000 0.037999 0.445384 0.487496 -0.014321 0.375169BET 0.037999 1.000000 0.016596 -0.021922 0.016283 0.017652BUX 0.445384 0.016596 1.000000 0.545900 0.027081 0.536316PX 0.487496 -0.021922 0.545900 1.000000 0.044288 0.497050SOFIX -0.014321 0.016283 0.027081 0.044288 1.000000 -0.003174WIG 0.375169 0.017652 0.536316 0.497050 -0.003174 1.000000

Correlation matrix for returnsCorrelation matrix for returns

The correlation analysis

Choosing the order of the variables using the F-test, market capitalization Choosing the order of the variables using the F-test, market capitalization and the efficiency of the market: ATX, WIG, PX, BUX, BET, SOFIX. and the efficiency of the market: ATX, WIG, PX, BUX, BET, SOFIX. Verify the sign and proportion of the spillover between the returns using Verify the sign and proportion of the spillover between the returns using the impulse response and variance decomposition. the impulse response and variance decomposition.

k

i

k

i ttitit XYY1 1 111

k

i

k

i ttitit YXX1 1 111

ATX WIG BUX PX BET SOFIXATX - 0.8864 0.7603 0.0710 0.9771 0.3061WIG 0.5379 - 0.3632 0.7832 0.7919 0.8956BUX 0.0954 0.4349 - 0.7776 0.8995 0.8116PX 0.6382 0.5534 0.5316 - 0.1943 0.3178BET 0.6461 0.0311 0.8891 0.2188 - 0.5890SOFIX 0.3594 0.2729 0.1126 0.8735 0.4195 -

Granger causality test for returnsDependent variable

Lags of variable

Lag length criteria suggests a specification including 1 lagLag length criteria suggests a specification including 1 lag

Verify the short-term interaction between returns using Granger causality test:Verify the short-term interaction between returns using Granger causality test:

The response of returns to shocks applied on the other marketsThe response of returns to shocks applied on the other markets

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_ATX to D_ATX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_ATX to D_WIG

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_ATX to D_BUX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_ATX to D_PX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_ATX to D_BET

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_ATX to D_SOFIX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_WIG to D_ATX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_WIG to D_WIG

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_WIG to D_BUX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_WIG to D_PX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_WIG to D_BET

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_WIG to D_SOFIX

.000

.004

.008

.012

1 2 3

Response of D_BUX to D_ATX

.000

.004

.008

.012

1 2 3

Response of D_BUX to D_WIG

.000

.004

.008

.012

1 2 3

Response of D_BUX to D_BUX

.000

.004

.008

.012

1 2 3

Response of D_BUX to D_PX

.000

.004

.008

.012

1 2 3

Response of D_BUX to D_BET

.000

.004

.008

.012

1 2 3

Response of D_BUX to D_SOFIX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_PX to D_ATX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_PX to D_WIG

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_PX to D_BUX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_PX to D_PX

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_PX to D_BET

-.002

.000

.002

.004

.006

.008

.010

.012

1 2 3

Response of D_PX to D_SOFIX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_BET to D_ATX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_BET to D_WIG

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_BET to D_BUX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_BET to D_PX

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_BET to D_BET

-.004

.000

.004

.008

.012

.016

1 2 3

Response of D_BET to D_SOFIX

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3

Response of D_SOFIX to D_ATX

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3

Response of D_SOFIX to D_WIG

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3

Response of D_SOFIX to D_BUX

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3

Response of D_SOFIX to D_PX

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3

Response of D_SOFIX to D_BET

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3

Response of D_SOFIX to D_SOFIX

Response to Cholesky One S.D. Innovations ± 2 S.E.

Variance decomposition for the returnsVariance decomposition for the returns

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

Days ahead D_ATX D_WIG D_PX D_BUX D_BET D_SOFIX1 100.0000 0.000000 0.000000 0.000000 0.000000 0.0000002 99.59105 0.044013 0.286347 0.007451 3.33E-06 0.0711393 99.58768 0.044011 0.287208 0.007801 0.000799 0.072503

Days ahead D_ATX D_WIG D_PX D_BUX D_BET D_SOFIX1 14.17161 85.82839 0.000000 0.000000 0.000000 0.0000002 14.10401 85.81295 0.021019 0.056347 0.004508 0.0011683 14.10415 85.81132 0.021926 0.056702 0.004724 0.001177





Variance decomposition for SOFIX returns

Variance decomposition for BUX returns

Variance decomposition for PX returns

Variance decomposition for ATX returns

Variance decomposition for WIG returns

Variance decomposition for BET returns

Methods in obtaining returns volatility

Obtained variances series for returns using GARCH(1,1) methodObtained variances series for returns using GARCH(1,1) method - the mean equation:- the mean equation:

ttt yy 1

21

21

2 ttt

- the conditional variance equation:- the conditional variance equation:

Using a EGARCH(1,1,1) method to estimate variance for SOFIX returnsUsing a EGARCH(1,1,1) method to estimate variance for SOFIX returns Conditional variance equation for the EGARCH:Conditional variance equation for the EGARCH:

2

)ln()ln(2

1

1

21

121

2

t

t

t

ttt

Advantages in using a EGARCH method: Advantages in using a EGARCH method: - the coefficients can be negative because - the coefficients can be negative because )ln( 2

t is modeled.is modeled.- the asymmetry of the EGARCH model capture the leverage effect. - the asymmetry of the EGARCH model capture the leverage effect.

Volatility - the correlation analyzeVolatility - the correlation analyze

Verify stationarity of the variance using the ADF and PP tests, the series Verify stationarity of the variance using the ADF and PP tests, the series are stationary at any significance level. are stationary at any significance level. Check the relation between the series using the matrix correlation and GrangerCheck the relation between the series using the matrix correlation and GrangerCausality test. Causality test.

ATX BET BUX SOFIX PX WIGATX - 0.6707 0.4989 0.1782 0.8455 0.8701

BET 0.5589 - 0.0259 0.3360 0.1367 0.0946

BUX 0.3143 0.6825 - 0.2474 0.0401 0.3882SOFIX 0.7626 0.9919 0.3906 - 0.9509 0.0285

PX 0.1647 0.9934 0.1262 0.1319 - 0.8961

WIG 0.7174 0.9941 0.2192 0.3314 0.4108 -

Granger causality test for varianceDependent variable

Lags of variable

ATX BET BUX PX SOFIX WIGATX 1.000000 -0.075787 0.461381 0.522018 -0.046720 0.179787

BET -0.075787 1.000000 0.062745 -0.035368 -0.033744 -0.166919

BUX 0.461381 0.062745 1.000000 0.675333 0.038740 0.332519

PX 0.522018 -0.035368 0.675333 1.000000 0.106245 0.347898

SOFIX -0.046720 -0.033744 0.038740 0.106245 1.000000 0.366838

WIG 0.179787 -0.166919 0.332519 0.347898 0.366838 1.000000

Correlation matrix for volatiles

Impulse response for volatility Impulse response for volatility

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

.00006

25 50 75 100 125 150 175 200

Response of D_ATXVARIANCE to D_ATXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

.00006

25 50 75 100 125 150 175 200

Response of D_ATXVARIANCE to D_WIGVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

.00006

25 50 75 100 125 150 175 200

Response of D_ATXVARIANCE to D_BUXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

.00006

25 50 75 100 125 150 175 200

Response of D_ATXVARIANCE to D_PXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

.00006

25 50 75 100 125 150 175 200

Response of D_ATXVARIANCE to D_BETVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

.00006

25 50 75 100 125 150 175 200

Response of D_ATXVARIANCE to D_SOFIXVARIANCE

-.000008

-.000004

.000000

.000004

.000008

.000012

.000016

25 50 75 100 125 150 175 200

Response of D_WIGVARIANCE to D_ATXVARIANCE

-.000008

-.000004

.000000

.000004

.000008

.000012

.000016

25 50 75 100 125 150 175 200

Response of D_WIGVARIANCE to D_WIGVARIANCE

-.000008

-.000004

.000000

.000004

.000008

.000012

.000016

25 50 75 100 125 150 175 200

Response of D_WIGVARIANCE to D_BUXVARIANCE

-.000008

-.000004

.000000

.000004

.000008

.000012

.000016

25 50 75 100 125 150 175 200

Response of D_WIGVARIANCE to D_PXVARIANCE

-.000008

-.000004

.000000

.000004

.000008

.000012

.000016

25 50 75 100 125 150 175 200

Response of D_WIGVARIANCE to D_BETVARIANCE

-.000008

-.000004

.000000

.000004

.000008

.000012

.000016

25 50 75 100 125 150 175 200

Response of D_WIGVARIANCE to D_SOFIXVARIANCE

-.000005

.000000

.000005

.000010

.000015

.000020

.000025

25 50 75 100 125 150 175 200

Response of D_BUXVARIANCE to D_ATXVARIANCE

-.000005

.000000

.000005

.000010

.000015

.000020

.000025

25 50 75 100 125 150 175 200

Response of D_BUXVARIANCE to D_WIGVARIANCE

-.000005

.000000

.000005

.000010

.000015

.000020

.000025

25 50 75 100 125 150 175 200

Response of D_BUXVARIANCE to D_BUXVARIANCE

-.000005

.000000

.000005

.000010

.000015

.000020

.000025

25 50 75 100 125 150 175 200

Response of D_BUXVARIANCE to D_PXVARIANCE

-.000005

.000000

.000005

.000010

.000015

.000020

.000025

25 50 75 100 125 150 175 200

Response of D_BUXVARIANCE to D_BETVARIANCE

-.000005

.000000

.000005

.000010

.000015

.000020

.000025

25 50 75 100 125 150 175 200

Response of D_BUXVARIANCE to D_SOFIXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

25 50 75 100 125 150 175 200

Response of D_PXVARIANCE to D_ATXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

25 50 75 100 125 150 175 200

Response of D_PXVARIANCE to D_WIGVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

25 50 75 100 125 150 175 200

Response of D_PXVARIANCE to D_BUXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

25 50 75 100 125 150 175 200

Response of D_PXVARIANCE to D_PXVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

25 50 75 100 125 150 175 200

Response of D_PXVARIANCE to D_BETVARIANCE

-.00001

.00000

.00001

.00002

.00003

.00004

.00005

25 50 75 100 125 150 175 200

Response of D_PXVARIANCE to D_SOFIXVARIANCE

-.00002

.00000

.00002

.00004

.00006

.00008

.00010

.00012

25 50 75 100 125 150 175 200

Response of D_BETVARIANCE to D_ATXVARIANCE

-.00002

.00000

.00002

.00004

.00006

.00008

.00010

.00012

25 50 75 100 125 150 175 200

Response of D_BETVARIANCE to D_WIGVARIANCE

-.00002

.00000

.00002

.00004

.00006

.00008

.00010

.00012

25 50 75 100 125 150 175 200

Response of D_BETVARIANCE to D_BUXVARIANCE

-.00002

.00000

.00002

.00004

.00006

.00008

.00010

.00012

25 50 75 100 125 150 175 200

Response of D_BETVARIANCE to D_PXVARIANCE

-.00002

.00000

.00002

.00004

.00006

.00008

.00010

.00012

25 50 75 100 125 150 175 200

Response of D_BETVARIANCE to D_BETVARIANCE

-.00002

.00000

.00002

.00004

.00006

.00008

.00010

.00012

25 50 75 100 125 150 175 200

Response of D_BETVARIANCE to D_SOFIXVARIANCE

-.00005

.00000

.00005

.00010

.00015

.00020

.00025

.00030

25 50 75 100 125 150 175 200

Response of D_SOFIXVARIANCE to D_ATXVARIANCE

-.00005

.00000

.00005

.00010

.00015

.00020

.00025

.00030

25 50 75 100 125 150 175 200

Response of D_SOFIXVARIANCE to D_WIGVARIANCE

-.00005

.00000

.00005

.00010

.00015

.00020

.00025

.00030

25 50 75 100 125 150 175 200

Response of D_SOFIXVARIANCE to D_BUXVARIANCE

-.00005

.00000

.00005

.00010

.00015

.00020

.00025

.00030

25 50 75 100 125 150 175 200

Response of D_SOFIXVARIANCE to D_PXVARIANCE

-.00005

.00000

.00005

.00010

.00015

.00020

.00025

.00030

25 50 75 100 125 150 175 200

Response of D_SOFIXVARIANCE to D_BETVARIANCE

-.00005

.00000

.00005

.00010

.00015

.00020

.00025

.00030

25 50 75 100 125 150 175 200

Response of D_SOFIXVARIANCE to D_SOFIXVARIANCE

Response to Cholesky One S.D. Innovations ± 2 S.E.

Variance Decomposition for volatilityVariance Decomposition for volatility

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

Period ATX WIG PX BUX BET SOFIX1 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000

30 98.28603 0.164898 0.025445 0.425067 0.343396 0.75516390 98.21085 0.181920 0.041278 0.452319 0.344078 0.769553


30 1.005268 87.43912 0.337155 1.220722 0.014976 0.93534190 9.585778 86.66214 0.287021 2.076229 0.031225 1.357606


30 31.21845 4.910377 61.72163 1.784894 0.001183 0.36346690 31.42500 5.438533 60.84827 1.925888 0.001301 0.361007


30 29.01829 6.690134 13.64212 50.32515 0.205289 0.11902190 29.81494 7.025592 13.85145 48.98721 0.207251 0.113559


30 0.236730 0.805854 0.233885 2.838162 95.52907 0.35630490 0.245924 1.730081 0.250796 3130038 94.27640 0.366764


30 0.131636 4.089252 0.035681 0.856636 0.048411 94.8383890 0.348825 8.306711 0.034418 1.198519 0.048603 90.06292

Variance Decomposition of BET volatility

Variance Decomposition of SOFIX volatility

Variance Decomposition of ATX volatility

Variance Decomposition of WIG volatility

Variance Decomposition of PX volatility

Variance Decomposition of BUX volatility

ConclusionsConclusions

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

ReferencesReferences

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.9510

4) Brooks, C (2002), “Introductory Econometrics for Finance”, Cambridge University Press5) 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. 683

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Coefficient Std. Error t-Statistic Prob. C(1) 0.160751 0.005633 28.53943 0.0000C(2) 0.056116 0.004273 13.13232 0.0000C(3) -0.033904 0.043034 -0.787851 0.4309C(4) 0.233037 0.080167 2.906890 0.0037C(5) 0.369370 0.012866 28.70958 0.0000

R-squared 0.988895 2173.383Adjusted R-squared 0.988864 1150.403S.E. of regression 121.3962 12.43941Sum squared resid 21471850 12.45749Log likelihood -9088.209 0.045170 Durbin-Watson stat

ATX=C(1)*BET+C(2)*BUX+C(3)*SOFIX+C(4)*PX+C(5)*WIGIncluded observations: 1462 after adjusting endpoints

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion

Sample(adjusted): 10/20/2000 5/29/2006Date: 06/20/07 Time: 02:42Method: Least SquaresDependent Variable: ATX Dependent Variable: BET

Method: Least SquaresDate: 06/20/07 Time: 02:38Sample(adjusted): 10/20/2000 5/29/2006Included observations: 1462 after adjusting endpointsBET=C(1)*ATX+C(2)*BUX+C(3)*SOFIX+C(4)*PX+C(5)*WIG

Coefficient Std. Error t-Statistic Prob.

C(1) 2.230612 0.078159 28.53943 0.0000C(2) -0.036820 0.016806 -2.190921 0.0286C(3) 2.854737 0.141828 20.12813 0.0000C(4) 0.603770 0.299075 2.018791 0.0437C(5) -1.450192 0.046399 -31.25499 0.0000R-squared 0.973051 Mean dependent var 3540.710Adjusted R-squared 0.972977 S.D. dependent var 2750.874S.E. of regression 452.2098 Akaike info criterion 15.06958Sum squared resid 2.98E+08 Schwarz criterion 15.08767Log likelihood -11010.87 Durbin-Watson stat 0.037582

Dependent Variable: BUXMethod: Least SquaresDate: 06/20/07 Time: 02:43Sample(adjusted): 10/20/2000 5/29/2006Included observations: 1462 after adjusting endpointsBUX=C(1)*ATX+C(2)*BET+C(3)*SOFIX+C(4)*PX+C(5)*WIG

Coefficient Std. Error t-Statistic Prob. C(1) 1.886040 0.143618 13.13232 0.0000C(2) -0.089182 0.040705 -2.190921 0.0286C(3) -3.807791 0.228730 -16.64756 0.0000C(4) 13.40919 0.306340 43.77223 0.0000C(5) -0.159655 0.093236 -1.712374 0.0870R-squared 0.987852 Mean dependent var 13089.12Adjusted R-squared 0.987819 S.D. dependent var 6376.682S.E. of regression 703.7783 Akaike info criterion 15.95422Sum squared resid 7.22E+08 Schwarz criterion 15.97230Log likelihood -11657.53 Durbin-Watson stat 0.065961

Dependent Variable: PXMethod: Least SquaresDate: 07/09/07 Time: 03:45Sample(adjusted): 10/20/2000 5/29/2006Included observations: 1462 after adjusting endpointsPX=C(1)*ATX+C(2)*BET+C(3)*BUX+C(4)*SOFIX+C(5)*WIG

Coefficient Std. Error t-Statistic Prob. C(1) 0.024744 0.008512 2.906890 0.0037C(2) 0.004620 0.002288 2.018791 0.0437C(3) 0.042362 0.000968 43.77223 0.0000C(4) 0.289009 0.011806 24.47900 0.0000C(5) 0.045756 0.005107 8.959594 0.0000R-squared 0.991982 Mean dependent var 856.4309Adjusted R-squared 0.991960 S.D. dependent var 441.1593S.E. of regression 39.55700 Akaike info criterion 10.19678Sum squared resid 2279850. Schwarz criterion 10.21486Log likelihood -7448.844 Durbin-Watson stat 0.059943

Engle Granger residual base cointegration testEngle Granger residual base cointegration test

Dependent Variable: SOFIXMethod: Least SquaresDate: 06/20/07 Time: 02:45Sample(adjusted): 10/20/2000 5/29/2006Included observations: 1462 after adjusting endpointsSOFIX=C(1)*ATX+C(2)*BET+C(3)*BUX+C(4)*PX+C(5)*WIG

Coefficient Std. Error t-Statistic Prob. C(1) -0.012560 0.015942 -0.787851 0.4309C(2) 0.076213 0.003786 20.12813 0.0000C(3) -0.041970 0.002521 -16.64756 0.0000C(4) 1.008339 0.041192 24.47900 0.0000C(5) -0.030242 0.009766 -3.096585 0.0020R-squared 0.959420 Mean dependent var 497.5816Adjusted R-squared 0.959309 S.D. dependent var 366.2858S.E. of regression 73.88751 Akaike info criterion 11.44638Sum squared resid 7954294 Schwarz criterion 11.46446Log likelihood -8362.303 Durbin-Watson stat 0.029330

Dependent Variable: WIGMethod: Least SquaresDate: 06/20/07 Time: 02:49Sample(adjusted): 10/20/2000 5/29/2006Included observations: 1462 after adjusting endpointsWIG=C(1)*ATX+C(2)*BET+C(3)*BUX+C(4)*SOFIX+C(5)*PX

Coefficient Std. Error t-Statistic Prob. C(1) 0.978186 0.034072 28.70958 0.0000C(2) -0.276768 0.008855 -31.25499 0.0000C(3) -0.012580 0.007347 -1.712374 0.0870C(4) -0.216194 0.069817 -3.096585 0.0020C(5) 1.141233 0.127376 8.959594 0.0000R-squared 0.920975 Mean dependent var 1872.073Adjusted R-squared 0.920758 S.D. dependent var 701.7897S.E. of regression 197.5536 Akaike info criterion 13.41331Sum squared resid 56862958 Schwarz criterion 13.43139Log likelihood -9800.130 Durbin-Watson stat 0.034693

Engle Granger residual base cointegration testEngle Granger residual base cointegration test

Dependent Variable: D_ATXMethod: ML - ARCH (Marquardt)Date: 06/20/07 Time: 03:07Sample(adjusted): 10/23/2000 5/29/2006Included observations: 1461 after adjusting endpointsConvergence achieved after 17 iterationsVariance backcast: ON

Coefficient Std. Error z-Statistic Prob. C 0.001524 0.000226 6.734204 0.0000

C 1.29E-05 2.00E-06 6.481563 0.0000ARCH(1) 0.125142 0.018366 6.813744 0.0000

GARCH(1) 0.765879 0.030141 25.40960 0.0000R-squared -0.002485 Mean dependent var 0.000999Adjusted R-squared -0.004549 S.D. dependent var 0.010543S.E. of regression 0.010567 Akaike info criterion -6.351087Sum squared resid 0.162692 Schwarz criterion -6.336612

Variance Equation Variance Equation

Dependent Variable: D_BUXMethod: ML - ARCH (Marquardt)Date: 06/20/07 Time: 03:11Sample(adjusted): 10/23/2000 5/29/2006Included observations: 1461 after adjusting endpointsConvergence achieved after 9 iterationsVariance backcast: ON


C 9.80E-06 2.83E-06 3.465744 0.0005ARCH(1) 0.070711 0.013044 5.420837 0.0000

GARCH(1) 0.881748 0.022236 39.65325 0.0000R-squared -0.000434 Mean dependent var 0.000739Adjusted R-squared -0.002494 S.D. dependent var 0.014391S.E. of regression 0.014409 Akaike info criterion -5.704374Sum squared resid 0.302492 Schwarz criterion -5.689899Log likelihood 4171.045 Durbin-Watson stat 1.945470

Variance Equation

Dependent Variable: D_BETMethod: ML - ARCH (Marquardt)Date: 06/20/07 Time: 03:09Sample(adjusted): 10/23/2000 5/29/2006Included observations: 1461 after adjusting endpointsConvergence achieved after 13 iterationsVariance backcast: ON


C 1.09E-05 1.69E-06 6.428384 0.0000ARCH(1) 0.202481 0.021477 9.428005 0.0000


Variance Equation

Dependent Variable: D_PXMethod: ML - ARCH (Marquardt)Date: 06/20/07 Time: 03:15Sample(adjusted): 10/23/2000 5/29/2006Included observations: 1461 after adjusting endpointsConvergence achieved after 18 iterationsVariance backcast: ON


C 1.35E-05 2.83E-06 4.776848 0.0000ARCH(1) 0.113726 0.017111 6.646257 0.0000


Variance Equation

Estimating the volatilitiesEstimating the volatilities

Dependent Variable: D_SOFIXMethod: ML - ARCH (Marquardt)Date: 06/20/07 Time: 03:13Sample(adjusted): 10/23/2000 5/29/2006Included observations: 1461 after adjusting endpointsConvergence achieved after 41 iterationsVariance backcast: ON


C 6.03E-07 1.34E-07 4.511134 0.0000ARCH(1) 0.106533 0.006070 17.55001 0.0000


Variance Equation

Dependent Variable: D_WIGMethod: ML - ARCH (Marquardt)Date: 06/20/07 Time: 03:16Sample(adjusted): 10/23/2000 5/29/2006Included observations: 1461 after adjusting endpointsConvergence achieved after 12 iterationsVariance backcast: ON


C 2.56E-06 1.02E-06 2.525784 0.0115ARCH(1) 0.036995 0.007936 4.661851 0.0000


Variance Equation

Estimating the volatilitiesEstimating the volatilities

integration of center and eastern european stock markets msc student iosif anaida coordinator...

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