Alterations in the financial markets of the Baltic countries and Russia in the period of economic downturn/Finansu rinku pokyciai Baltijos salyse ir Rusijoje ekonominio nuosmukio laikotarpiu.

Dubinskas, Petras ; Stunguriene, Stanislava

1. Foreword

The explosive changes in the global equity markets are assessed by financial analysis and researchers from different viewpoints with some focusing on the consequences, and others--on the reasons. Such significant changes often permit of a chance to test and validate different capital market theories on investor behaviour, asset valuation methods and mechanisms, market valuation, fluctuations, etc. Having regard to different factors of the changes in the business environment from a strategic standpoint (Daugeliene and Marcinkeviciene 2009; Vida and Obadia 2008; Thomsen 2008; Serbanica et al. 2009; Saboniene 2009; Ruzevicius 2009) and peculiarities of the recent integration process (Melnikas 2008a, 2008b; Karagiannis and Feridun 2009; Misztal 2009), the research of the status of the capital markets of different regions must employ the methods meeting the specific objectives of the researchers.

Most market research exercises have been undertaken and research papers concerned normal market functioning conditions with observable usual capital market developments with sustainable growth of globalization impact (Gudonavicius et al. 2009; Chlivickas et al. 2009; Ciegis et al. 2008; Ginevicius and Cirba 2009). These theories, presumptions and findings have been found, however, hardly applicable under extreme, or even catastrophic market conditions (Arbel et al. 1988, Dubinskas 2009). It might be partly justifiable to conclude that such research works have lost their true value.

Most authors designate financial turmoil as the main reason moving financial markets worldwide. The reasons for the appearance of such turmoil are most often divided into two categories: macroeconomic and microeconomic. The reasons of macroeconomic character are ordinarily related to the changes in the State budget, interest and inflation rates (Pilinkus and Boguslauskas 2009; Paskevicius and Dubinskas 2009; Wang, Yang and Li 2007). Insurance of investment portfolios, speculative operations on derivative financial instrument markets, risky acquisitions and "bubbles" caused by long-lasting speculations are attributed to microeconomic reasons (Malliaris and Urrutia 1992; Chuang et al. 2009; Girdzijauskas et al. 2009). And nevertheless, this point of view has received some criticism referring to the impossibility to verify the reliability of the conclusions (Dong and Liu 2007; Roll 1988). Since the reasons for the financial turmoil have not yet been ultimately clarified, it has become a really challenging task to identify the impact of such shocks in the financial markets upon the efficiency of capital markets which is often perceived as the response of financial instrument prices to publicly accessible information. It if often concluded that the price of a company's share reflects the entire information about the company. Still, there might be cases where capital markets become volatile even without receiving any material information. This raises doubts as the existence of any link between the financial turmoil and the market efficiency (Friedman 1990).

The present article focuses on the changes and developments of the equity markets in the three Baltic States--Lithuania, Latvia and Estonia and Russia starting from 2008. The authors of the present article have advanced a hypothesis that the trends of an abrupt plunge and subsequent stabilisation of equity prices that were clearly discernable during the 1987 crisis are also characteristic for the current financial crisis. The survey underlying the present paper was conducted on the basis of the following assumptions: 1) both crises affected financial markets of several states; 2) a characteristic feature for the inception of the crises is an abrupt fall in equity prices; 3) indications of stabilisation in financial markets become observable before financial experts conclude the end of the financial crisis.

2. Research methodology

To confirm the hypothesis on the similarities of the general trends during the two major international financial crisis, the authors employed empiric tests developed on the basis of Granger causality tests: 1) Granger causality tests, and 2) cointegration tests.

Granger causality tests are the principal tests used in forecasting methods by applying time series. One time series {[Y.sub.t]} has an impact upon another time series {[X.sub.t]}. Granger has proved that the value of the variable X is best forecasted using the lagged values of the Y time series. This definitely requires the appropriate information and the lagged values of the {[X.sub.t]} time series be known.

The link between the corresponding values of Y and X time series is recorded as follows:

[Y.sub.t] = [[delta].sub.0] + [m.summation over (i=1)][[alpha].sub.i][X.sub.t-i] + [m.summation over (j=1)][[beta].sub.j][Y.sub.t-j] + [[mu].sub.t]. (1)

An assumption is then made that the X value affects the corresponding Y value only if [a.sub.i] [not equal to] 0 :

[Y.sub.t] = [c.sub.0] + [m.summation over (t=1)][a.sub.i][X.sub.t-i] + [m.summation over (j=1)][b.sub.j][Y.sub.t--j] + [e.sub.t]. (2)

The appearance of both events is followed by concluding the presence of a feedback loop (Schmidt 1976; Pierce and Haugh 1977; Geweke et al. 1983; Guilkey and Salemi 1982; Gao and Tian 2009). The F-statistics value computed for the purpose of the causality test assesses the equation presented above (2):

[F.sub.1] = ([SSE.sub.r] - [SSE.sub.f])/m / [SSE.sub.f]/(T - 2m - 1), (3)

where: [SSE.sub.r]--(sum of the sguarded error) squared error in simplified model; [SSE.sub.f]--(sum of the sguarded error) squared error in the full model; T--number of observed samples; m--number of lags;

In the most general case the squared error SSE shall be computed as follows:

SSE = [summation][e.sup.2] [summation [([??] [??]).sup.2], - (4)

where: Y--actual value; [??]--computed value.

An assumption is made that [F.sub.1] corresponds to [chi square]/m and Wald test (Boguslauskas 2007; Studenmund 2006).

Since errors may be both positive and negative there is a possibility that the average error is equal to zero, despite significant observable deviations in both directions. Squared deviations are computed with a view to avoiding this situation. The best regression line is obtained when the total of squared deviations is the least. For this reason the regression analysis is sometimes also referred to as least-squares regression.

Based on this calculation methodology American researchers have been examining potential dependences between different international equity markets under financial crisis conditions (Malliaris and Urrutia 1992; Aktan et al. 2009, Blume et al. 1989; Horobet and Lupu 2009; De Gooijer and Sivarajasingham 2008; Ruxanda and Stoenescu 2009).

Cointegration tests are used in the cases requiring identification of the relation between changes in prices in individual markets. For instance, Engle and Granger (1987) have proved that in the case two non-stationar variables are coinegrated, the autoregression vector in the first differences is not fixed. Suppose, natural logarithms at one stock exchange and another stock exchange, respectively, [LnP.sub.1t] and [LnP.sub.2t] non-stationar, and the firs differences of the natural logarithms of each price are stationar, it might be concluded that the prices are integrated from the first line that is recorded as 7(1). The first differences of the logarithms are recorded:

[LnP.sub.1t] - [LnP.sub.1,t-1] or Ln [P.sub.1t]/[P.sub.1,t-1] and [LnP.sub.2t] - [LnP.sub.2,t-1] or Ln [P.sub.2t]/[P.sub.2,t-1].

Where each price is from the first line 7(1), and the linear stationary combination between the prices of the two stock exchanges is established, it shall be recognised that both price sets are cointegrated. The cointegrations have a direct influence upon the equations (1) and (2) of the Granger causality tests. Where conintegration is fixed the calculations according to the equations (1) and (2) become meaningless. Therefore, prior to starting using causality tests in the theory of econometrics it is highly recommended to verify the cointegration between the primary data sequences (Kedaitis 2009; Lapin 1987; Adams et al. 1993).

Having established the data cointegration it is necessary to seek to identify other research methods or accordingly adjust the models already created. For instance, a number of cointegration testing methods were proposed by Engle and Granger (1987). They developed statistical tests, compiled tables of critical values and compared the applicability of the different tests.

The simplest cointegration test is the Durbin-Watson regression cointegration CRDW suitable to be used in first series systems. But the critical values of the test are extremely sensitive to parameters the values whereof are below zero. In practice, CRDW is not a recommended method for the examination of economic data, however, the test is still applied in certain cases, the results obtained are analysed, summarised and presented in research papers (Malliaris and Urrutia 1992).

Rather worthy of notice is the Dickey-Fuller cointegration test that was applied for the purpose of examination of the price changes in the markets during the 1987 financial crisis. The test was based on the regression cointegration idea: initially performed calculations according to the formula (5) were followed by an examination of the residual errors of the Dickey-Fuller regression (6).

[LnP.sub.1t] = [c.sub.0] + [c.sub.1][LnP.sub.2t] + [[epsilon].sub.t], (5)

[[epsilon].sub.t] - [[epsilon].sub.t-1] = -[b.sub.1][[epsilon].sub.t-1] + [[mu].sub.1]. (6)

This method is based on the hypothesis, that [b.sub.1] = 0, and [LnP.sub.1t] and [LnP.sub.2t] are not reintegrated. This method is recommended for the investigation of price developments in the pre-crisis period, since the examination of financial markets during the crisis or in the post-crisis period requires the identification of models specifically tailored for the situation, or requires creation of new models (Christopoulus and Leon-Ledesma 2008; Gelper and Croux 2007; Rublikova 2003; Dufour and Jouini 2006). In the event of no cointegration between the variables, clearly Granger causality tests should be prioritised and regression equations must be adjusted simultaneously according to the peculiarities of the problem addressed (equations 1 and 2). Therefore, where, in Dickey-Fuller statistics zero is rejected, the conclusion is drawn up that the variables cointegrate and the Granger regression theory is not applicable. Therefore it is necessary to adjust the regression equations (1.2) by supplementing them by residual errors of the adjusting regression. The residual regression error in the Granger regression equation is recorded as an additional independent variable (7, 8):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (7)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (8)

where [[??].sub.t-1] is the residual error of regression equation (5).

Many other researchers for the purpose of examining the phenomena of the same crisis used the regressive analysis methods (Roll 1988; Gennotte and Leland 1990; Arbel et al. 1988). For instance, R. Roll established that the major plunging on the Asian markets (except Japanese) started on 19 October 1987. The latter fall was caused primarily by minor slumps in the markets of some European states, later on in the Northern America and finally, in Japan (Roll 1988).

Practical experience has shown that the application of different mathematical methods for investigation of the same economic phenomena yields different results which, in addition, may be differently interpreted.

3. Research of the developments in the Baltic financial markets

In relation to any survey of the developments in financial markets, and specifically under extraordinary conditions it is of vital importance to provide a description of the initial data collected and the appropriate interpretation of the same. For example, Malliaris and Urrutia (1992) presented the data about six equity markets (New York S&P 500, Tokyo Nikkei, London FT-30, Hon Kong Hang Seng, Singapore Straits Times and Australia All Ordinaries), with specific focus on the developments in the period from 1 May 1987 to 31 March 1988. All data was divided into three periods: a) prior to the crisis; b) during the crisis; c) the period after the crisis. Having established the cointegration degree, and considering the results obtained, the authors selected an appropriate causality test. Similar surveys were performed by other authors too (An and Zhao 2008; Pan and Dai 2008; Beine et al. 2008).

The beginning of the financial downturn in the Baltic States (Lithuania, Latvia and Estonia) and Russia should be marked as the end of Q3, 2008 that was characterised by most prominent negative changes in the capital market; while the beginning of Q3, 2009 should be considered the start of the way to recovery. The data used for the survey of the situation in financial markets are divided into three periods: 1) the pre-crisis period (01-02-2008 31-08-2008), 2) the crisis period (01-09-2008--30-05-2009); 3) and the post-crisis period (01-06-2009--31-12-2009).

Based on the precedent survey of the financial crisis of 1987 (Malliaris and Urrutia 1992), the authors of the present article chose to use the Granger causality testing methodology. To be able to apply the Granger causality test first it was necessary to verify the degree of cointegration of the indices of the main equity markets in each of the country (OMX Vilnius, OMX Riga, OMX Tallinn, RTS). For that purpose the authors used the Dickey-Fuller and Johansen testing methodology. Both methodologies demonstrated a strong cointegration between the changes in the indices of all equity markets irrespective of the period analysed (i.e., pre-crisis, during the crisis, post-crisis). In all cases the T-statistics exceeded the critical value. The strongest cointegration was observable in the crisis period, and the weakest-after the crisis.

Since the data cointegrate the Granger causality test may be used only having accordingly adjusted the regression equations (1, 2). The equations were supplemented by residual errors of the cointegrating regression (7, 8): the vector error adjusting type was chosen by developing the vector autoregression model by means of the EViews application. The exercise was based on the assumption that the zero hypothesis on the absence of causality is confirmed, where [chi square] does not exceed the critical value. In the opposite case the existence of causality between the financial markets concerned is concluded.

4. Results of the research

The fluctuation of the stock market indexes during pre-crisis, crisis and post-crisis periods in the Lithuania, Latvia, Estonia and Russia was selected as a research object. Data cointegreation level was determined using Dickey-Fuller and Johansen methods.

In the determination of cointegration level according to the Dickey-Fuller method the significance level is 5%. Critical values were calculated from statistical data using EViews program: pre-crisis (-2.882), during crisis (-2.877), post-crisis (-2.881). Data cointegration results are interpreted according T-Statistic values (see Tables 1, 2 and 3).

In the determination of cointegration level according to the Johansen method the significance level is 5% (see Tables 4, 5 and 6).

Data cointegration was approved using both Dickey-Fuller and Johansen methods, but the valuation of capital markets causality is related with causality direction (see Table 7).

The results of the survey demonstrated, in the period preceding the financial downturn, the OMX Vilnius index was specifically affected, and in particular by OMX Riga and RTS (see Table 7). No causality was established between the Latvian and Estonian markets. The high attention must be paid to the Russian capital market's low influence to the Latvian and Estonian capital markets. The assessment of all four markets has led to a conclusion that the Lithuanian equity market was the most passive, while Latvian market was most active. This might justify a conclusion that the financial downturn of the Baltic States that started in September 2008 was primarily initiated by the Latvian financial market. Moreover, the decline of Lithuanian capital market was initiated by the Russian capital market too. The Russian capital market was affected by Latvian capital market. Therefore, it can be stated that financial downturn started in Latvia. After that the crisis reached the Russia and finally Estonia and Lithuania.

In view of a financial crisis cases of existence of causality between markets become more frequent. In this respect, specifically noticeable is the Latvian market--the most active in the period preceding the market and the most passive of the three during the crisis, though the Estonian market lost some of its activity either. Therefore, it might be presumed that it was there markets that were mostly affected by the outburst of the financial downturn. Furthermore, in view of a financial crisis most investors relate their interests and expectations with larger markets, that under crisis conditions are considered more reliable and tend to recover quicker. This is clearly evidenced by the case of the Lithuanian financial market that were the most passive in the period preceding the downturn, became the most active in the mid of the crisis and survived the equity plunge period comparatively painlessly. The similar situation is with the Russian market. Taken all these factors and considerations into account it is highly probable that the end of the financial downturn started in Lithuania and Russia initiating the recovery trends in the smaller Baltic States (Latvia and Estonia).

The end of the financial downturn restored the initial situation. The Latvian market is gaining its activity (even exceeding the Estonian). The Lithuanian and the Russian markets are less active than in the crisis period, however, more active than at the time preceding the crisis.

5. Conclusions

1. The research results evaluated causality changes in Lithuanian, Latvian, Estonian and Russian financial markets during pre-crisis (01/02/2008--31/08/2008), crisis (01/09/2008--30/05/2009) and post crisis (01/06/2009--31/12/2009) periods.

2. The survey concluded that in all cases the developments in the period prior to the financial downturn affected the OMX Vilnius index that was mostly influenced by OMX Riga index and RTS index (see Table No. 7). No causality was established between the Latvian and Estonian capital markets. The assessment of all four capital markets yields a conclusion that the Lithuanian financial market was most passive, while the Latvian market was most active. In the opinion of the authors, the financial downturn trends of the Baltic markets that started in September 2008 originated in the Latvian financial markets.

3. In the mid of the financial crisis the Latvian market was most passive, though noted for its activity in the pre-crisis times. The results of the survey also showed that the Estonian market had lost some of its activity. Thus, the financial downturn to the largest extent affected the Latvian and Estonian markets. This is clearly evidenced by the case of the Lithuanian and Russian markets that became the most active in the mid of the crisis and survived the equity plunge period comparatively painlessly, thus confirming that in view of a financial crisis the interests and expectations of most investors are related to larger markets that are normally considered more reliable and resilient.

4. The end of the financial downturn restored the initial situation: the Latvian market is gaining its activity (in some respects even exceeding the Estonian), and the Lithuanian (together with Russian market) market is less active than in the crisis period, however, more active than at the time preceding the crisis.

5. The results of the causality of the financial markets of three financial markets confirmed the hypothesis that the analysis of the 2008-2009 financial crisis may employ the econometric methods applied for the analysis of the 1987 crisis by identifying the change in the trends of the pre-crisis, crisis and post-crisis causality.

doi: 10.3846/tede.2010.31

Received 20 November 2009; accepted 5 August 2010

References

Adams, A.; Bloomfield, D.; Booth, P.; England, P. 1993. Investment Mathematics and Statistics. Graham & Tritman.

Aktan, B.; Mandaci, P. E.; Kopurlu, B. S.; Ersener, B. 2009. Behaviour of Emerging Stock Markets in the Global Financial Meltdown: Evidence from bric-a, African Journal of Business Management 3(9): 396-404.

An, J. W.; Zhao, L. F. 2008. Study on Integration Trend of Fluctuation of Hong Kong Stock Market and Shanghai and Shenzhen Stock Market, in Call for paper proceedings of 2008 International Conference on Management Science and Engineering, 1052-1060.

Arbel, A.; Carvell, S.; Postnieks, E. 1988. The Smart Crash of October 19th, Harvard Business Review (3): 124-136.

Beine, M.; Capelle-Blancard, G.; Raymond, H. 2008. International Nonlinear Causality between Stock Markets, European Journal of Finance 14(8): 663-686. doi:10.1080/13518470802042112

Blume, M. E.; MacKinlay, A. C.; Terker, B. 1989. Order Imbalances and Stock Price Movements on October 19 and 20, 1987, Journal of Finance (44): 827-848. doi:10.2307/2328612

Boguslauskas, V. 2007. Ekonometrika. Kaunas: Technologija.

Chlivickas, E.; Petrauskaite, N.; Ambrusevic, N. 2009. Leading priorities for development of the high technologies market, Journal of Business Economics and Management 10(4): 321-328. doi:10.3846/1611 1699.2009.10.321-328

Christopoulus, D. K.; Leon-Ledesma, M. A. 2008. Testing for Granger (non)-causality in a Time-varying Coefficient VAR Model, Journal of Forecasting 27(4): 293-303. doi:10.1002/for. 1060

Chuang, C. C.; Kuan, C. M.; Lin, H. Y. 2009. Causality in Quantiles and Dynamic Stock Return-volume Relations, Journal of Banking & Finance 33(7): 1351-1360. doi:10.1016/j.jbankfin.2009.02.013

Ciegis, R.; Gavenauskas, A.; Petkeviciute, N.; Streimikiene, D. 2008. Ethical values and sustainable development: Lithuanian experience in the context of globalisation, Technological and Economic Development of Economy 14(1): 29-37. doi:10.3846/2029-0187.2008.14.29-37

Daugeliene, R.; Marcinkeviciene, R. 2009. Brain circulation: theoretical considerations, Inzinerine Ekonomika--Engineering Economics (3): 49-57.

De Gooijer, J. G.; Sivarajasingham, S. 2008. Parametric and Nonparamertic Granger Causality Testing: Linkages between International Stock Markets, Physica A-statistical Mechanics and Its Applications 387(11): 2547-2560. doi:10.1016/j.physa.2008.01.033

Dong, Z.; Liu, M. 2007. Empirical Analysis of Interaction between Securities Market, Banking System and Nation's Economic Growth, in Proceedings of the 2007 Conference on Management Science and Engineering, Finance Analysis Section, 147-150.

Dubinskas, P. 2009. Problems of the application of deterministic models in securities portfolio optimization in times of market shocks, Current Issues of Business and Law (4): 37-54.

Dufour, J. M.; Jouini, T. 2006. Finite sample simulation-based Inference in VAR models with application to granger causality testing, Journal of Econometrics 135(1-2): 229-254. doi:10.1016/j.jeco nom.2005.07.025

Engle, R.; Granger, C. W. 1987. Co-integration and error correction: representation, estimation, testing, Econometrica (55): 251-276. doi:10.2307/1913236

Friedman, B. 1990. Financial markets and monetary economics, National Bureau of Economic Research Reporter (Spring 1990): 1-7.

Gao, W.; Tian, Z. 2009. Learning granger causality graphs for multivariate nonline time series, Journal of Systems Science and Systems Engineering 18(1): 38-52.

Gelper, S.; Croux, C. 2007. Multivariate out-of-sample tests for granger causality, Computational Statistics & Data Analysis 51(7): 3319-3329. doi:10.1016/j.csda.2006.09.021

Gennotte, G.; Leland, H. 1990. Market liquidity, hedging and crashes, American Economic Review (5): 999-1021.

Geweke, J.; Meese, R.; Dent, W. 1983. Comparing alternative tests of causality in temporal systems, Journal of Econometrics 21(2): 161-194. doi:10.1016/0304-4076(83)90012-X

Ginevicius, R.; Cirba, S. 2009. Additive measurement of market concentration, Journal of Business Economics and Management 10(3): 191-198. doi:10.3846/1611-1699.2009.10.191-198

Girdzijauskas, S.; Streimikiene, D.; Cepinskis, J.; Moskaliova, V.; Jurkonyte, E.; Mackevicius, R. 2009. Formation of economic bubbles: causes and possible preventions, Technological and Economic Development of Economy 15(2): 267-280. doi:10.3846/1392-8619.2009.15.267-280

Gudonavicius, L.; Bartoseviciene, V.; Saparnis, G. 2009. Imperatives for enterprise strategists, Inzinerine Ekonomika--Engineering Economics (1): 75-82.

Guilkey, D. K.; Salemi, M. K. 1982. Small sample properties of three tests for granger causal ordering in a bivariate stochastic system, The Review of Economics and Statistics 64(11): 668-680. doi:10.2307/1923951

Horobet, A.; Lupu, R. 2009. Are capital markets integrated? A test of information transmission within the European Union, Romanian Journal of Economics Forecasting 10(2): 64-80.

Karagiannis, S.; Feridun, M. 2009. Growth effects of information and communication technologies: empirical evidence from the Enlarged EU, Transformations in Business & Economics 8(2): 86-99.

Kedaitis, V. 2009. Fundaments of Correlation and Regression Analysis. Vilnius: Vilniaus universiteto leidykla.

Lapin, L. L. 1987. Statistics for Modern Business Decisions. Harcourt Brace Jovanovich, Inc.

Malliaris, A. G.; Urrutia, J. L. 1992. The international crash of october 1987: Causality tests, Journal of Financial and Quantitative Analysis 27(3): 353-364. doi:10.2307/2331324

Melnikas, B. 2008a. Integration processes in the Baltic region: the new form of regional transformations in the European Union, Inzinerine Ekonomika--Engineering Economics (5): 54-64.

Melnikas, B. 2008b. The knowledge-based economy in the European Union: innovations, networking and transformation strategies, Transformations in Business & Economics 7(3): 170-192.

Misztal, P. 2009. International competitiveness of the Baltic states in the transformation period: Lithuania, Latvia and Estonia, Transformations in Business & Economics 8(3): 21-35.

Pan, Y.; Dai, Y. Y. 2008. Investigation of Non-linear Granger Causality between China Mainland Stock Market and Hong Kong H-share Market, in International Conference on Management Science & Engineering (15th), Vol. I and II, Conference Proceedings, 1214-1219.

Paskevicius, A.; Dubinskas, P. 2009. The Lithuanian Equity Market in 1993-2008: Reasons for its Decline, Ekonomika [Economics] (87): 72-79.

Pierce, D. A.; Haugh, L. D. 1977. Causality in temporal systems: characterizations and a survey, Journal of Econometrics 5(5): 265-293. doi:10.1016/0304-4076(77)90039-2

Pilinkus, D.; Boguslauskas, V. 2009. The short-run relationship between stock market prices and macroeconomic variables in Lithuania: an application of the impulse response function, Inzinerine Ekonomika--Engineering Economics (5): 26-33.

Roll, R. 1988. The International Crash of October 1987, The Financial Analysts Journal 44(9): 19-35. doi:10.2469/faj.v44.n5.19

Rublikova, E. 2003. Testing for cointegration using engle-granger methodology, in Proceedings of the 21st International Conference Mathematical Methods in Economics, 235-237.

Ruxanda, G.; Stoenescu, S. 2009. Bivariate and multivariate cointegration and their application in stock markets, Economic Computation and Economic Cybernetics Studies and Research 43(4): 17-31.

Ruzevicius, J. 2009. Environmental management systems and tools analysis, Inzinerine Ekonomika Engineering Economics (4): 49-59.

Saboniene, A. 2009. Lithuanian export competitiveness: comparison with other Baltic States, Inzinerine Ekonomika--Engineering Economics (2): 49-57.

Schmidt, R. J. 1976. Econometrics. New York, N. Y.: Marcel Dekker, Inc.

Serbanica, D.; Constantinescu, M.; Caescu, S. C. 2009. The influence of the global economic crisis on the strategic marketing instruments used by companies on the Romanian B2b Market, Transformations in Business & Economics 8(3): 44-59.

Studenmund, A. H. 2006. Using Econometrics. A practical Guide, 5th ed. Pearson. Addison Wesley.

Thomsen, J. 2008. Processes of Localization and Institutionalization of Local Managers in Economic Functions in Danish Owned Subsidiaries in Estonia, Latvia and Lithuania around the 21(St) Century, Journal of Business Economics and Management 9(4): 279-287. doi:10.3846/1611-1699.2008.9.279-287

Vida, I.; Obadia, C. 2008. The dark side of internationalisation: dysfunctional behaviours in foreign subsidiaries, Transformations in Business & Economics 7(2): 130-143.

Wang, Z. J.; Yang, J.; Li, Q. 2007. Interest rate linkages in the eurocurency market: contemporaneous and out-of-samle granger causality tests, Journal of International Money and Finance (26): 86-103. doi:10.1016/j.jimonfin.2006.10.005

Petras Dubinskas (1), Stanislava Stunguriene (2)

International Business School at Vilnius University, Sauletekio al. 22, LT-10225 Vilnius, Lithuania

E-mail: (1) petrasd@tiscali.it; (2) ststunguriene@centras.lt

Petras DUBINSKAS. Master, Lecturer and Assistant. Department of Finance. International Business School at Vilnius University. First degree in international relations and politics sciences, The Institute of International Relations and Politics Sciences at Vilnius University (2002). Master of Science in International Business School at Vilnius University (2005). Research visits to Cordoba University (Spain, 2008), Savonia University of Applied Sciences (Finland, 2009), The Polytechnical Institute of Beja (Portugal, 2009), La Rochelle Business School (France, 2010). Author of 4 scientific articles. Research interests: financial and investment management, financial econometrics, financial engineering, optimization of financial deicions.

Stanislava STUNGURIENE. Doctor, Associate Professor. Financial Department of International Business School at Vilnius University. First degree and master in economic engineering, Vilnius University (1972). Doctor (1982). Author of about 30 scientific articles. Research interests: information technologies, operations management, quantitative analysis in economics and management, optimal financial decisions.

Table 1. Cointegration of index returns in pre-crisis period

Period before the crisis (01/02/2008--31/08/2008)

  Dependant     Independent
  variable        variable     T-statistics      Results

 OMX Vilnius      OMX Riga        -10.837      cointegrate
 OMX Vilnius    OMX Tallinn       -11.261      cointegrate
 OMX Vilnius        RTS           -10.707      cointegrate
  OMX Riga      OMX Vilnius       -16.127      cointegrate
 OMX Tallinn    OMX Vilnius       -9.703       cointegrate
     RTS        OMX Vilnius       -11.167      cointegrate
  OMX Riga      OMX Tallinn       -16.161      cointegrate
  OMX Riga          RTS           -16.181      cointegrate
 OMX Tallinn      OMX Riga        -9.324       cointegrate
     RTS          OMX Riga        -11.336      cointegrate
 OMX Tallinn        RTS           -9.225       cointegrate
     RTS        OMX Tallinn       -11.027      cointegrate

Table 2. Cointegration of index returns during the crisis period

During the crisis (01/09/2008--30/05/2009)

  Dependant      Independent
  variable        variable      T-statistics      Results

 OMX Vilnius      OMX Riga         -12.920      cointegrate
 OMX Vilnius     OMX Tallinn       -13.015      cointegrate
 OMX Vilnius         RTS           -12.959      cointegrate
  OMX Riga       OMX Vilnius       -14.929      cointegrate
 OMX Tallinn     OMX Vilnius       -12.998      cointegrate
     RTS         OMX Vilnius       -12.439      cointegrate
  OMX Riga       OMX Tallinn       -14.317      cointegrate
  OMX Riga           RTS           -14.857      cointegrate
 OMX Tallinn      OMX Riga         -12.380      cointegrate
     RTS          OMX Riga         -12.342      cointegrate
 OMX Tallinn         RTS           -12.772      cointegrate
     RTS         OMX Tallinn       -12.272      cointegrate

Table 3. Cointegration of index returns in post-crisis period

The period after the crisis (01/06/2009--31/12/2009)

 Dependant      Independent
  variable       variable      T-statistics      Results

OMX Vilnius      OMX Riga         -10.190      cointegrate
OMX Vilnius     OMX Tallinn       -13.139      cointegrate
OMX Vilnius         RTS           -10.106      cointegrate
  OMX Riga      OMX Vilnius       -11.500      cointegrate
OMX Tallinn     OMX Vilnius       -14.038      cointegrate
    RTS         OMX Vilnius       -10.824      cointegrate
  OMX Riga      OMX Tallinn       -11.540      cointegrate
  OMX Riga          RTS           -11.434      cointegrate
OMX Tallinn      OMX Riga         -10.935      cointegrate
    RTS          OMX Riga         -10.846      cointegrate
OMX Tallinn         RTS           -11.370      cointegrate
    RTS         OMX Tallinn       -11.383      cointegrate

Table 4. Data Cointegration in pre-crisis period

Period before the crisis (01/02/2008--31/08/2008)

   Number of
 cointegration       Trace      Critical
   equations      statistics      value        Result

       0            116.11        47.85     cointegrate
       1             75.34        29.79     cointegrate
       2             39.98        15.49     cointegrate
       3             14.48        3.84      cointegrate

Table 5. Data Cointegration during the crisis period

During the crisis (01/09/2008-30/05/2009)

  Number of
cointegration       Trace      Critical
  equations      statistics      value        Result

      0            133.40        47.85     cointegrate
      1             86.94        29.79     cointegrate
      2             51.02        15.49     cointegrate
      3             19.49        3.84      cointegrate

Table 6. Data Cointegration in post-crisis period

The period after the crisis (01/06/2009--31/12/2009)

  Number of        Trace      Critical
cointegration   statistics      value        Result
  equations

      0           150.80        47.85     cointegrate
      1            90.66        29.79     cointegrate
      2            44.19        15.49     cointegrate
      3            17.69        3.84      cointegrate

Table 7. Assessment of causality of the Lithuanian, Latvian, Estonian
and Russian financial markets

                                    Period before      During the
                                      the crisis         crisis

Causality direction                  (01/02/208--    (01/09/2008--
                                     31/08/2008)      30/05/2009)

X statistics (critical value 5.99)

Latvia Lithuania                        13.92             0.15
Estonia [right arrow] Lithuania          6.54             4.75
Lithuania [right arrow] Latvia           5.04             8.62
Estonia [right arrow] Latvia             5.98            13.96
Lithuania [right arrow] Estonia          5.47            16.37
Latvia [right arrow] Estonia             3.19             1.03
Lithuania [right arrow] Russia           4.55             3.40
Latvia [right arrow] Russia              6.48             5.94
Estonia [right arrow] Russia             7.03             2.21
Russia [right arrow] Lithuania          12.52            16.26
Russia [right arrow] Latvia              3.19            17.90
Russia [right arrow] Estonia             0.29             4.56

                                      The period
                                      after the
                                        crisis

Causality direction                 (01/06/2009--
                                     31/12/2009)

X statistics (critical value 5.99)

Latvia Lithuania                         6.44
Estonia [right arrow] Lithuania          1.97
Lithuania [right arrow] Latvia           8.65
Estonia [right arrow] Latvia            13.76
Lithuania [right arrow] Estonia          2.60
Latvia [right arrow] Estonia            21.49
Lithuania [right arrow] Russia           1.17
Latvia [right arrow] Russia              3.56
Estonia [right arrow] Russia             8.01
Russia [right arrow] Lithuania           2.34
Russia [right arrow] Latvia              9.95
Russia [right arrow] Estonia             5.06