The use of transfer entropy to analyse the comovements of European Union stock markets: a dynamical analysis in times of crises
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Understanding the linkages among stock markets holds great importance for investors, policymakers and portfolio managers. When considering the integration of international stock markets and given they are complex systems, it is important to understand how they are related and how they influence each other. Studying data from 25 European Union stock market indices, this piece of research aims to evaluate the dynamics of influence among them. In terms of method, a non-linear approach has been applied, based on transfer entropy with static and dynamic analysis. As the main finding, a strongly influential relationship between some indices should be highlighted. The static analysis allows us to infer that central and western European Union countries are the main influencers, while the dynamic analysis leads us to the conclusion that the relationships between the stock markets have changed over time, revealing their dynamism. The results obtained have several implications. For instance, for investors and portfolio managers, the information about comovements is relevant for diversification purposes and for their decisions on where to make their investments, build portfolio strategies and manage risks; however, for policymakers, the constant monitoring of stock markets may detect increases in the connection between markets, which could be understood as signs of instability.
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Fundação para a Ciência e a Tecnologia
Números do Financiamento grants UIDB/05064/2020 and UIDB/04007/2020 -
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Números do Financiamento Finance Code 001
Referências
Aslam, F., Ferreira, P., Mughal, K. S., & Bashir, B. (2021). Intraday volatility spillovers among European financial markets during COVID-19. International Journal of Financial Studies, 9(1), 1–19. https://doi.org/10.3390/ijfs9010005
Assaf, A., Bilgin, M. H., & Demir, E. (2022). Using transfer entropy to measure information flows between cryptocurrencies. Physica A: Statistical Mechanics and Its Applications, 586, 126484. https://doi.org/10.1016/j.physa.2021.126484
Barnett, L., Barrett, A. B., & Seth, A. K. (2009). Granger Causality and Transfer Entropy Are equivalent for Gaussian Variables. Physical Review Letters, 103(23). https://doi.org/10.1103/PhysRevLett.103.238701
Behrendt, S., Dimpfl, T., Peter, F. J., & Zimmermann, D. J. (2019). RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy. SoftwareX, 10, 100265. https://doi.org/10.1016/j.softx.2019.100265
Bekaert, G., Harvey, C. R., Lundblad, C. T., & Siegel, S. (2013). The European Union, the Euro, and equity market integration. Journal of Financial Economics, 109(3), 583–603. https://doi.org/10.1016/j.jfineco.2013.03.008
Bentes, S. R. (2015). On the integration of financial markets: How strong is the evidence from five international stock markets? Physica A: Statistical Mechanics and Its Applications, 429, 205–214. https://doi.org/10.1016/j.physa.2015.02.070
Boţoc, C., & Anton, S. G. (2020). New empirical evidence on CEE’s stock markets integration. World Economy, 43(10), 2785–2802. https://doi.org/10.1111/twec.12961
Burdekin, R. C. K., Hughson, E., & Gu, J. (2018). A first look at Brexit and global equity markets. Applied Economics Letters, 25(2), 136–140. https://doi.org/10.1080/13504851.2017.1302057
Büttner, D., & Hayo, B. (2011). Determinants of European stock market integration. Economic Systems, 35(4), 574–585. https://doi.org/10.1016/j.ecosys.2010.10.004
Cantuche, J. (2021). The economy of the European Union in times of COVID-19. Revista Galega de Economia, 30(1). https://doi.org/10.15304/rge.30.1.7663.
Caruso, A., Reichlin, L., & Ricco, G. (2019). Financial and fiscal interaction in the Euro Area crisis: This time was different. European Economic Review, 119, 333–355. https://doi.org/10.1016/j.euroecorev.2019.08.002
Chakrabarti, P., Jawed, M. S., & Sarkhel, M. (2021). COVID-19 pandemic and global financial market interlinkages: a dynamic temporal network analysis. Applied Economics, 53(25), 2930–2945. https://doi.org/10.1080/00036846.2020.1870654
Chang, E., Cheng, J., & Khorana, A. (2000). An examination of herd behavior in equity markets: an international perspective, Journal of Banking & Finance, 24(10), 1651-1679. https://doi.org/10.1016/S0378-4266(99)00096-5
Daugherty, M. & Jithendranathan, T. (2015). A study of linkages between frontier markets and the U.S. equity markets using multivariate GARCH and transfer entropy, Journal of Multinational Financial Management, 32, 95-115, https://doi.org/10.1016/j.mulfin.2015.10.003.
Dias, J. G., & Ramos, S. B. (2013). A core-periphery framework in stock markets of the euro zone. Economic Modelling, 35, 320–329. https://doi.org/10.1016/j.econmod.2013.07.013
Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets, The Economic Journal, 119 (534), 158-171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
Diks, C., & Fang, H. (2017). Transfer entropy for nonparametric granger causality detection: An evaluation of different resampling methods. Entropy, 19(7), 1–38. https://doi.org/10.3390/e19070372
Dimpfl, T., & Peter, F. (2013). Using transfer entropy to measure information flows between financial markets, Studies in Nonlinear Dynamics & Econometrics, 17(1), 85-102. https://doi.org/10.1515/snde-2012-0044
Dionisio, A., Menezes, R., & Mendes, D. A. (2004). Mutual information: A measure of dependency for nonlinear time series. Physica A: Statistical Mechanics and Its Applications, 344(1–2), 326–329. https://doi.org/10.1016/j.physa.2004.06.144
Duttilo, P., Gattone, S. A., & Di Battista, T. (2021). Volatility modeling: An overview of equity markets in the euro area during covid-19 pandemic. Mathematics, 9(11). https://doi.org/10.3390/math9111212
Fang, H., Chung, C. P., Lee, Y. H., & Yang, X. (2021). The Effect of COVID-19 on Herding Behavior in Eastern European Stock Markets. Frontiers in Public Health, 9(July), 1–9. https://doi.org/10.3389/fpubh.2021.695931
Ferreira, P., Dionísio, A., Almeida, D., Quintino, D., & Aslam, F. (2021). A new vision about the influence of major stock markets in CEEC indices: a bidirectional dynamic analysis using transfer entropy. Post-Communist Economies, 00(00), 1–16. https://doi.org/10.1080/14631377.2021.2006498
Gabriel, A. S. (2012). Evaluating the Forecasting Performance of GARCH Models. Evidence from Romania. Procedia - Social and Behavioral Sciences, 62, 1006–1010. https://doi.org/10.1016/j.sbspro.2012.09.171
Gabriel, V. M. de S. M., & Pires, J. R. P. (2015). Financial Crisis and Stock Market Linkages. Revista Galega de Economía, 23(4). https://doi.org/10.15304/rge.23.4.2793
Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3), 424–438. https://doi.org/10.1017/ccol052179207x.002
Granger, C. W. J., Huangb, B. N., & Yang, C. W. (2000). A bivariate causality between stock prices and exchange rates: Evidence from recent Asianflu. Quarterly Review of Economics and Finance, 40(3), 337–354. https://doi.org/10.1016/s1062-9769(00)00042-9
Guedes, E. F., Ferreira, P., Dionísio, A., & Zebende, G. F. (2019). An econophysics approach to study the effect of BREXIT referendum on European Union stock markets. Physica A: Statistical Mechanics and Its Applications, 523, 1175–1182. https://doi.org/10.1016/j.physa.2019.04.132
Horvath, R., & Petrovski, D. (2013). International stock market integration: Central and south eastern europe compared. Economic Systems, 37(1), 81–91. https://doi.org/10.1016/j.ecosys.2012.07.004
Huynh, T. L. D., Nasir, M. A., Vo, X. V., & Nguyen, T. T. (2020). “Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet. North American Journal of Economics and Finance, 54(August). https://doi.org/10.1016/j.najef.2020.101277
Iglesias, E. M. (2015). Value at Risk of the main stock market indexes in the European Union (2000-2012). Journal of Policy Modeling, 37(1), 1–13. https://doi.org/10.1016/j.jpolmod.2015.01.006
Jizba, P., Kleinert, H., & Shefaat, M. (2012). Rényi’s information transfer between financial time series. Physica A: Statistical Mechanics and Its Applications, 391(10), 2971–2989. https://doi.org/10.1016/j.physa.2011.12.064
Kenourgios, D., & Samitas, A. (2011). Equity market integration in emerging Balkan markets. Research in International Business and Finance, 25(3), 296–307. https://doi.org/10.1016/j.ribaf.2011.02.004
Kenourgios, D., Samitas, A., & Paltalidis, N. (2011). Financial crises and stock market contagion in a multivariate time-varying asymmetric framework. Journal of International Financial Markets, Institutions and Money, 21(1), 92–106. https://doi.org/10.1016/j.intfin.2010.08.005
Kim, S., Ku, S., Chang, W., Chang, W., Chang, W., & Song, J. W. (2020). Predicting the Direction of US Stock Prices Using Effective Transfer Entropy and Machine Learning Techniques. IEEE Access, 8, 111660–111682. https://doi.org/10.1109/ACCESS.2020.3002174
Korbel, J., Jiang, X., & Zheng, B. (2019). Transfer entropy between communities in complex financial networks. Entropy, 21(11), 1–13. https://doi.org/10.3390/e21111124
Kuang, P. (2021). Measuring information flow among international stock markets: An approach of entropy-based networks on multi time-scales, Physica A: Statistical Mechanics and its Applications, 577, https://doi.org/10.1016/j.physa.2021.126068.
Kwon, O., & Yang, J.-S. (2008). Information flow between stock indices, Europhysics Letters, 82(6), 68003.
Lee, J. W., & Nobi, A. (2018). State and Network Structures of Stock Markets Around the Global Financial Crisis. Computational Economics, 51(2), 195–210. https://doi.org/10.1007/s10614-017-9672-x
Lizier, J., Heinzle, J., Horstmann, A., Haynes, J.-D., & Prokopenko, M. (2011). Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity. Journal of Computational Neuroscience, 30(1), 85–107. https://doi.org/10.1007/s10827-010-0271-2
Mensi, W., Boubaker, F. Z., Al-Yahyaee, K. H., & Kang, S. H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25(November 2017), 230–238. https://doi.org/10.1016/j.frl.2017.10.032
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 (Switzerland), 12(2). https://doi.org/10.3390/su12020535
Mylonidis, N., & Kollias, C. (2010). Dynamic European stock market convergence: Evidence from rolling cointegration analysis in the first euro-decade. Journal of Banking and Finance, 34(9), 2056–2064. https://doi.org/10.1016/j.jbankfin.2010.01.012
Nardo, M., Ossola, E., & Papanagiotou, E. (2022). Financial integration in the EU28 equity markets: Measures and drivers, Journal of Financial Markets, 57, 100633. https://doi.org/10.1016/j.finmar.2021.100633
Niţoi, M., & Pochea, M. M. (2019). What drives European Union stock market co-movements? Journal of International Money and Finance, 97, 57–69. https://doi.org/10.1016/j.jimonfin.2019.06.004
Niţoi, M., & Pochea, M. M. (2020). Time-varying dependence in European equity markets: A contagion and investor sentiment driven analysis. Economic Modelling, 86(August 2018), 133–147. https://doi.org/10.1016/j.econmod.2019.06.007
Pirgaip, B., Ertuğrul, H. M., & Ulussever, T. (2021). Is portfolio diversification possible in integrated markets? Evidence from South Eastern Europe. Research in International Business and Finance, 56(January), 1–11. https://doi.org/10.1016/j.ribaf.2021.101384
Ramchand, L., & Susmel, R. (1998). Volatility and cross correlation across major stock markets. Journal of Empirical Finance, 5(4), 397–416. https://doi.org/10.1016/S0927-5398(98)00003-6
Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464. https://doi.org/10.1103/PhysRevLett.85.461
Sehgal, S., Gupta, P., & Deisting, F. (2017). Assessing time-varying stock market integration in Economic and Monetary Union for normal and crisis periods. European Journal of Finance, 23(11), 1025–1058. https://doi.org/10.1080/1351847X.2016.1158727
Sensoy, A., Sobaci, C., Sensoy, S., & Alali, F. (2014). Effective transfer entropy approach to information flow between exchange rates and stock markets. Chaos, Solitons and Fractals, 68, 180–185. https://doi.org/10.1016/j.chaos.2014.08.007
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379–423, 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x
Škrinjarić, T. (2019). Stock market reactions to brexit: Case of selected CEE and SEE stock markets. International Journal of Financial Studies, 7(1). https://doi.org/10.3390/ijfs7010007
Škrinjarić, T. (2020). CEE and SEE equity market return spillovers: Creating profitable investment strategies. Borsa Istanbul Review, 20, S62–S80. https://doi.org/10.1016/j.bir.2020.09.006
Stoupus, N., & Kiohos, A. (2022). Euro area stock markets integration: Empirical evidence after the end of 2010 debt crisis, Finance Research Letters, 46(B), 102423. https://doi.org/10.1016/j.frl.2021.102423
Tevdovski, D., & Stojkoski, V. (2021). What is Behind Extreme Negative Returns co-movement in the South Eastern European Stock Markets?, Scientific Annals of Economics and Business, 68(1), 43-61. https://doi.org/10.47743/saeb-2021-0003
Tilfani, O., Ferreira, P., Dionisio, A., & Youssef El Boukfaoui, M. (2020). EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients. Journal of Risk and Financial Management, 13(5), 91. https://doi.org/10.3390/jrfm13050091
Wang, P., & Moore, T. (2008). Stock market integration for the transition economies: time-varying conditional correlation approach, The Manchester School, 76(s1), 116-133. https://doi.org/10.1111/j.1467-9957.2008.01083.x
Yi, E., Cho, Y., Sohn, S., & Ahn, K. (2021). After the Splits: Information Flow between Bitcoin and Bitcoin Family. Chaos, Solitons and Fractals, 142, 110464. https://doi.org/10.1016/j.chaos.2020.110464