Integration in Central European capital markets in the context of the global COVID-19 pandemic

Abstract

Research background: Covid-19 pandemic had a strong impact on the economy and capital market. In times of crisis, it is important for investors to be able to diversify their investment portfolio in order to mitigate risk. However, the growing trend towards capital market integration may make it ineffective. Research on financial integration, during the Covid-19 period, has started to develop, mainly in major global capital markets. It is, therefore, important to extend this research to other capital markets.

The purpose of the article: This contribution aims to analyze financial integration in the stock indexes of the capital markets of Austria (ATX), Slovenia (SBITOP), Hungary (BUDAPEST SE), Lithuania (OMX VILNIUS), Poland (WIG), the Czech Republic (PX PRAGUE), Russia (MOEX) and Serbia (BELEX 15), in the context of the global pandemic (COVID-19).

Methods: To measure the unit roots in the time series, we used ADF, PP, and KPSS tests, and Clemente et al. (1998) test to detect structural breaks. To ana-lyse financial integration, we applied the Gregory and Hansen integration test, and to validate the robustness of results, we use the impulse-response function (IRF) methodology, with Monte Carlo simulations, as they provide a dynamic analysis generated from the VAR model estimates.

Findings & Value added: The results suggest very significant levels of integration, which decreases the chances of portfolio diversification in the long-term. Evidence shows 47 pairs of integrated stock market indexes (out of 56 possible). The stock indexes ATX, BUDAPESTE SE, BELEX 15 show financial integration with all other indexes. On the contrary, the index of OMX VILNIUS shows only 3 integrations. Results also show that most of the significant structural breaks occurred in March 2020. The analysis of the relationship between markets, in the short term, shows positive/negative co-movements, with statis-tical significance and with a persistence longer than one week.

Keywords

COVID-19, capital market, financial integration, portfolio diversification, financial crisis

References

1. Ali, M., Alam, N., & Rizvi, S. (2020). Coronavirus (COVID-19) ? An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance. doi: 10.1016/j.jbef.2020.100341. DOI: https://doi.org/10.1016/j.jbef.2020.100341
   View in Google Scholar
2. Aziakpono, M. J. (2006). Financial integration amongst the SACU countries: Evidence from interest rate pass-through analysis. Journal for Studies in Economics and Econometrics, 30(2), 1-23. doi: 10.1111/pirs.12550. DOI: https://doi.org/10.1080/10800379.2006.12106405
   View in Google Scholar
3. Baruník, J., & Vácha, L. (2013). Contagion among Central and Eastern European stock markets during the financial crisis. Finance a Uver 63(5), 443-453.
   View in Google Scholar
4. Bremus, F., & Kliatskova, T. (2020). Legal harmonization, institutional quality, and countries? external positions: A sectoral analysis. Journal of International Money and Finance, 107. doi: 10.1016/j.jimonfin.2020.102217. DOI: https://doi.org/10.1016/j.jimonfin.2020.102217
   View in Google Scholar
5. Caporale, G. M., Gil-Alana, L. A., & Poza, C. (2020). High and low prices and the range in the European stock markets: A long-memory approach. Research in International Business and Finance, 52. doi: 10.1016/j.ribaf.2019.101126. DOI: https://doi.org/10.1016/j.ribaf.2019.101126
   View in Google Scholar
6. Carausu, N., Filip, B., Cigu, E., & Carmen, T. (2017). Contagion of capital markets in CEE countries: Evidence from Wavelet analysis. Emerging Markets Finance and Trade, 54(5). doi: 10.1080/1540496X.2017.1410129. DOI: https://doi.org/10.1080/1540496X.2017.1410129
   View in Google Scholar
7. Nicusor Dumitru ? Bogdan Florin Filip ? Elena Cigu ? Toderascu Sandu Carmen
   View in Google Scholar
8. Clemente, J., Monta?és, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), 175-182. doi: 10.1016/S0165-1765(98)00052-4. DOI: https://doi.org/10.1016/S0165-1765(98)00052-4
   View in Google Scholar
9. Chiaramonti, D., & Maniatis, K. (2020). Security of supply, strategic storage and Covid19: Which lessons learnt for renewable and recycled carbon fuels, and their future role in decarbonizing transport? Applied Energy, 271. doi: 10.1016/j.apenergy.2020.115216. DOI: https://doi.org/10.1016/j.apenergy.2020.115216
   View in Google Scholar
10. Cole, M. A., Elliott, R. J. R., & Liu, B. (2020). The impact of the Wuhan Covid-19 lockdown on air pollution and health: A machine learning and augmented synthetic control approach. Environmental and Resource Economics, 76(4), 553-580. doi: 10.1007/s10640-020-00483-4. DOI: https://doi.org/10.1007/s10640-020-00483-4
    View in Google Scholar
11. Dias, R., da Silva, J. V., & Dionísio, A. (2019). Financial markets of the LAC region: Does the crisis influence the financial integration? International Review of Financial Analysis, 63, 160-173. doi: 10.1016/j.irfa.2019.02.008. DOI: https://doi.org/10.1016/j.irfa.2019.02.008
    View in Google Scholar
12. Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072. doi: 10.2307/1912517. DOI: https://doi.org/10.2307/1912517
    View in Google Scholar
13. Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251. doi: 10.2307/1913236. DOI: https://doi.org/10.2307/1913236
    View in Google Scholar
14. Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99-126. doi: 10.1016/0304-4076(69)41685-7. DOI: https://doi.org/10.1016/0304-4076(69)41685-7
    View in Google Scholar
15. Griffith, R., Levell, P., & Stroud, R. (2020). The impact of COVID?19 on share prices in the UK*. Fiscal Studies, 41(2), 363-369. doi: 10.1111/1475-5890.12226. DOI: https://doi.org/10.1111/1475-5890.12226
    View in Google Scholar
16. Grigaliuniene, Z., Celov, D., & Hartwell, C. A. (2020). The more the Merrier? The reaction of euro area stock markets to new members. Journal of International Financial Markets, Institutions and Money, 66. doi: 10.1016/j.intfin. 2020.101195. DOI: https://doi.org/10.1016/j.intfin.2020.101195
    View in Google Scholar
17. Grubel, H. G. (1968). Internationally diversified portfolios: Welfare gains and capital flows. American Economic Review, 58(5), 867-868. doi: 10.1126/scien ce.151.3712.867-a.
    View in Google Scholar
18. Hao, N., Wang, H. H., & Zhou, Q. (2020). The impact of online grocery shopping on stockpile behaviour in Covid-19. China Agricultural Economic Review, 12(3), 459-470. doi: 10.1108/CAER-04-2020-0064. DOI: https://doi.org/10.1108/CAER-04-2020-0064
    View in Google Scholar
19. Hašková, S., & Vochozka, M. (2018). Duality in cyclical trends in European Union confirmed. SAGE Open, 8(1), 1-7. doi: 10.1177/2158244017753268. DOI: https://doi.org/10.1177/2158244017753268
    View in Google Scholar
20. Hoffmann, P., Kremer, M., & Zaharia, S. (2020). Financial integration in Europe through the lens of composite indicators. Economics Letters, 194. doi: 10.1016/j.econlet.2020.109344. DOI: https://doi.org/10.1016/j.econlet.2020.109344
    View in Google Scholar
21. Horák, J., & Krulický, T. (2019). Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company. In J. Horák (Ed.). SHS web of conferences: innovative economic symposium 2018 - milestones and trends of world economy (IES2018). EDP Sciences: Les Ulis, France. doi: 10.1051/shsconf/20196101006. DOI: https://doi.org/10.1051/shsconf/20196101006
    View in Google Scholar
22. Horák, J., Vrbka, J., & Šuleř, P. (2020). Support vector machine methods and artificial neural networks used for the development of bankruptcy prediction models and their comparison. Journal of Risk and Financial Management, 13(3). doi: 10.3390/jrfm13030060. DOI: https://doi.org/10.3390/jrfm13030060
    View in Google Scholar
23. Horvath, R., & Petrovski, D. (2012). International stock market integration: Central and South Eastern Europe compared. SSRN Electronic Journal. doi: 10.2139/ ssrn.2078238. DOI: https://doi.org/10.2139/ssrn.2078238
    View in Google Scholar
24. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259. doi: 10.1016/0165-1765(80)90024-5. DOI: https://doi.org/10.1016/0165-1765(80)90024-5
    View in Google Scholar
25. Johansen, S. (1988). Statistical analysis of cointegrated vectors. Journal of Economic Dynamics and Control, 12(2?3), 231-254. doi: 10.1016/0165-1889(88) 90041-3. DOI: https://doi.org/10.1016/0165-1889(88)90041-3
    View in Google Scholar
26. Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147. doi: 10.1016/0304-4076(95)01753-4. DOI: https://doi.org/10.1016/0304-4076(95)01753-4
    View in Google Scholar
27. Krarup, T. (2019). Money and the ?level playing field?: the epistemic problem of European financial market integration. New Political Economy, 1-16. doi: 10.1080/13563467.2019.1685959. DOI: https://doi.org/10.1080/13563467.2019.1685959
    View in Google Scholar
28. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shinb, Y. (1992). Testing the null hypothesis of stationary against the alternative of a unit root. Journal of Econometrics, 54(1), 159-178. doi: 10.1016/0304-4076(92)90104-Y. DOI: https://doi.org/10.1016/0304-4076(92)90104-Y
    View in Google Scholar
29. Lăzăroiu, G., Horák, J., & Valášková, K. (2020). Scaring ourselves to death in the time of COVID-19: Pandemic awareness, virus anxiety, and contagious fear. Linguistic and Philosophical Investigations, 19, 114-120. doi: 10.22381/LPI1 920208. DOI: https://doi.org/10.22381/LPI1920208
    View in Google Scholar
30. Lee, E.-J. (2017). Intra- and inter-regional portfolio diversification strategies under regional market integration: Evidence from U.S. global banks. International Review of Financial Analysis, 54, 1-22. doi: 10.1016/j.irfa.2017.08.001. DOI: https://doi.org/10.1016/j.irfa.2017.08.001
    View in Google Scholar
31. Levy, H., & Sarnat, M. (1970). International diversification of investment portfolios. American Economic Review, 60(4), 668-675. doi: 10.2307/1818410.
    View in Google Scholar
32. Liu, H., Manzoor, A., Wang, C., Zhang, L., & Manzoor, Z. (2020). The COVID-19 outbreak and affected countries stock markets response. International Journal of Environmental Research and Public Health, 17(8). doi: 10.3390/ijerph17082 800. DOI: https://doi.org/10.3390/ijerph17082800
    View in Google Scholar
33. Lucey, B. M., & Voronkova, S. (2008). Russian equity market linkages before and after the 1998 crisis: Evidence from stochastic and regime-switching cointegration tests. Journal of International Money and Finance, 27(8), 1303-1324. doi: 10.1016/j.jimonfin.2008.07.004. DOI: https://doi.org/10.1016/j.jimonfin.2008.07.004
    View in Google Scholar
34. Lütkepohl, H., & Saikkonen, P. (1997). Impulse response analysis in infinite order cointegrated vector autoregressive processes. Journal of Econometrics, 81(1), 127-157. doi: 10.1016/S0304-4076(97)00037-7. DOI: https://doi.org/10.1016/S0304-4076(97)00037-7
    View in Google Scholar
35. Milos, L. R., Hatiegan, C., Milos, M. C., Barna, F. M., & Botoc, C. (2020). Multifractal detrended fluctuation analysis (MF-DFA) of stock market indexes. Empirical evidence from seven Central and Eastern European markets. Sustainability, 12(2). doi: 10.3390/su12020535. DOI: https://doi.org/10.3390/su12020535
    View in Google Scholar
36. Moagar-Poladian, S., Clichici, D., & Stanciu, C. V. (2019). The comovement of exchange rates and stock markets in Central and Eastern Europe. Sustainability, 11(14). doi: 10.3390/su11143985. DOI: https://doi.org/10.3390/su11143985
    View in Google Scholar
37. Nsoesie, E., Okanyene, B.R., Yiyao L., Barnoon, L., & Brownstein, J. (2020). Analysis of hospital traffic and search engine data in Wuhan China indicates early disease activity in the Fall of 2019. Collections: HMS Scholarly Articles. Harvard Medical School.
    View in Google Scholar
38. Özer, M., Kam?şl?, M., & Kam?şl?, S. (2016). The analysis of volatility spillovers between the German and Central and Eastern European (CEE) stock markets by using frequency domain causality test. In Europe and Asia: economic integration prospects. CEMAFI International Association, 165-179.
    View in Google Scholar
39. Perron, P., & Phillips, P. C. B. (1988). Testing for a unit root in a time series regression. Biometrika, 2(75), 335-346. doi: 10.1080/07350015.1992.10509923. DOI: https://doi.org/10.1093/biomet/75.2.335
    View in Google Scholar
40. Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. doi: 10.1016/S0165-1765(97)00214-0. DOI: https://doi.org/10.1016/S0165-1765(97)00214-0
    View in Google Scholar
41. Pietrzak, M. B., Fałdziński, M., Balcerzak, A. P., Meluzín, T., & Zinecker, M. (2017). Short-term shocks and long-term relationships of interdependencies among Central European capital markets. Economics and Sociology, 10(1), 61-77. doi: 10.14254/2071-789X.2017/10-1/5. DOI: https://doi.org/10.14254/2071-789X.2017/10-1/5
    View in Google Scholar
42. Rapaccini, M., Saccani, N., Kowalkowski, C., Paiola, M., & Adrodegari, F. (2020). Navigating disruptive crises through service-led growth: the impact of COVID-19 on Italian manufacturing firms. Industrial Marketing Management, 88, 225-237. doi: 10.1016/j.indmarman.2020.05.017. DOI: https://doi.org/10.1016/j.indmarman.2020.05.017
    View in Google Scholar
43. Saadat, S., Rawtani, D., & Hussain, C. M. (2020). Environmental perspective of COVID-19. Science of the Total Environment, 728. doi: 10.1016/j.scitotenv. 2020.138870. DOI: https://doi.org/10.1016/j.scitotenv.2020.138870
    View in Google Scholar
44. Seo, J. (2020). Randomization tests for equality in dependence structure. Journal of Business & Economic Statistics, Advance online publication. doi: 10.1080/ 07350015.2020.1753527.
    View in Google Scholar
45. Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280-283. doi: 10.1016/j. jbusres.2020.05.059. DOI: https://doi.org/10.1016/j.jbusres.2020.05.059
    View in Google Scholar
46. Šuleř, P., & Machová, V. (2020). Financial stability in Central European coun-tries: case of the Visegrad four. In F. Uslu (Ed.). Proceedings of the 7th in-ternational conference on education and social sciences (INTCESS 2020). Istanbul: International Organization Center of Academic Research, 1218-1223.
    View in Google Scholar
47. Stoica, O., Diaconasu, D.-E., & Socoliuc, O. R. (2015). Dilemma: regional or international interdependencies in Central and Eastern European stock markets. Procedia Economics and Finance, 20, 601-609. doi: 10.1016/s2212-5671(15) 00114-8 DOI: https://doi.org/10.1016/S2212-5671(15)00114-8
    View in Google Scholar
48. Syllignakis, M., & Kouretas, G. P. (2006). Long and short-run linkages in CEE stock markets: implications for portfolio diversification and stock market integration. SSRN Electronic Journal. doi: 10.2139/ssrn.910507 DOI: https://doi.org/10.2139/ssrn.910507
    View in Google Scholar
49. Tong, C., Chen, J., & Buckle, M. J. (2018). A network visualization approach and global stock market integration. International Journal of Finance and Economics, 23(3), 296-314. doi: 10.1002/ijfe.1617 DOI: https://doi.org/10.1002/ijfe.1617
    View in Google Scholar
50. Tsay, R. S. (2010). Analysis of financial time series. Hoboken, New Jersey: John Willey & Sons. DOI: https://doi.org/10.1002/9780470644560
    View in Google Scholar
51. Vochozka, M., Horák, J., & Krulický, T. (2020). Innovations in management forecast: time development of stock prices with neural net-works. Marketing and Management of Innovations, 2, 324-339. DOI: https://doi.org/10.21272/mmi.2020.2-24
    View in Google Scholar
52. Voronkova, S. (2004). Equity market integration in Central European emerging markets: a cointegration analysis with shifting regimes. International Review of Financial Analysis, 13(5), 633-647. doi: 10.1016/j.irfa.2004.02.017. DOI: https://doi.org/10.1016/j.irfa.2004.02.017
    View in Google Scholar
53. Vrbka, J., & Rowland, Z. (2017). Stock price development forecasting using neural networks. In J. Váchal, M. Vochozka, J. Horák (Eds.). SHS web of conferences - innovative economic symposium 2017: strategic partnership in international trade. Les Ulis: EDP Sciences. doi: 10.1051/shsconf/20173901032. DOI: https://doi.org/10.1051/shsconf/20173901032
    View in Google Scholar
54. Vrbka, J., Rowland, Z., & Šuleř, P. (2019). Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance. In J. Horák (Ed.). SHS web of conferences: innovative economic symposium 2018 - milestones and trends of world economy (IES2018). Les Ulis: EDP Sciences. doi: 10.1051/shsconf/20196101031. DOI: https://doi.org/10.1051/shsconf/20196101031
    View in Google Scholar
55. Williams, C. C. (2020). Impacts of the coronavirus pandemic on Europe's tourism industry: addressing tourism enterprises and workers in the undeclared economy. International Journal of Tourism Research. Advance online publication. doi: 10.1002/jtr.2395. DOI: https://doi.org/10.1002/jtr.2395
    View in Google Scholar
56. Zeren, F., & Hizarci, A. (2020). The impact of COVID-19 coronavirus on stock markets: evidence from selected countries. Journal of Accounting and Finance Reviews, 3(1), 78-84. doi: 10.32951/mufider.706159. DOI: https://doi.org/10.32951/mufider.706159
    View in Google Scholar
57. Zinecker, M., Balcerzak, A. P., Fałdziński, M., Meluzín, T., & Pietrzak, M. B. (2016). Application of DCC-GARCH model for analysis of interrelations among capital markets of Poland, Czech Republic and Germany. In M. Reiff & P. Gezik (Ed.). Proceedings of the international scientific conference quantitative methods in economics multiple criteria decision making XVIII. Vratn: Letra Interactive, 416-421.
    View in Google Scholar