O uso da entropía de transferencia para analizar os comovementos dos mercados bolsistas da Unión Europea: unha análise dinámica en tempos de crise
Contido principal do artigo
Resumo
Comprender os vínculos entre os mercados de valores reviste gran importancia para os investidores, os responsables políticos e os xestores de carteiras. Ao considerar a integración dos mercados bolsistas internacionais e dado que son sistemas complexos, é importante entender como se relacionan e como se inflúen mutuamente. Estudando datos de 25 índices bolsistas da Unión Europea, esta investigación pretende avaliar a dinámica de influencia entre eles. En canto ao método, aplicouse un enfoque non lineal, baseado na entropía de transferencia con análise estática e dinámica. Como principal achado, cabe destacar unha relación de forte influencia entre algúns índices. A análise estática permite inferir que os países do centro e oeste da Unión Europea son os principais influentes, mentres que a análise dinámica lévanos á conclusión de que as relacións entre os mercados bolsistas cambiaron co tempo, revelando o seu dinamismo. Os resultados obtidos teñen varias implicacións. Por exemplo, para os investidores e xestores de carteiras, a información sobre os comovementos é relevante a efectos de diversificación e para as súas decisións sobre onde realizar os seus investimentos, construír estratexias de carteira e xestionar os riscos; con todo, para os responsables políticos, o seguimento constante dos mercados bolsistas pode detectar aumentos na conexión entre os mercados, que poderían entenderse como signos de inestabilidade.
Palabras chave
Detalles do artigo
plugins.generic.funding.fundingData
-
Fundação para a Ciência e a Tecnologia
plugins.generic.funding.funderGrants grants UIDB/05064/2020 and UIDB/04007/2020 -
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
plugins.generic.funding.funderGrants Finance Code 001
Citas
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