Modelling volatility spillovers, cross-market correlation and co-movements between stock markets in European Union: an empirical case study
Purpose – This article examines volatility spillovers, cross-market correlation, and comovements between selected developed and former communist emerging stock markets in the European Union. Modelling the behavioural dynamics of European stock markets represents a vital topic in a fascinating context, but also a current challenge of great interest.
Research Methodology – We propose to estimate and model volatility using GARCH family models for selected European markets. We aim to explore volatility movement, presence of leverage effect/ asymmetry in selected financial markets.
Findings – The econometric approach includes GARCH (1, 1) models for the sample period from 1, January 2000 to 12, July 2018. The empirical results revealed that exists a significant presence of volatility clustering in all selected financial markets except Poland and Croatia. The empirical analysis also indicates that both recent and past news generate a considerable impact on present volatility.
Research limitations – Our empirical study has certain limitations regarding the relatively small number of only eight stock markets.
Practical implications – It can provide a useful perspective for researchers, academics, investors, investment managers, decision-makers, and scientists.
Originality/Value – The empirical analysis is focused on 8 European stock markets, which are classified as developed (Spain, UK, Germany, and France) and emerging (Poland, Hungary, Croatia, and Romania).
This work is licensed under a Creative Commons Attribution 4.0 International License.
Becker, R., Clements, A., & O’Neill, R. (2018). A multivariate kernel approach to forecasting the variance covariance of stock market returns. Econometrics, 6(1), 1–27. https://doi.org/10.3390/econometrics6010007
Ben Slimane, F., Mehanaoui, M., & Kazi, I. A. (2013). How does the financial crisis affect volatility behaviour and transmission among European stock markets? International Journal of Financial Studies, 1(3), 81–101. https://doi.org/10.3390/ijfs1030081
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Czapkiewicz, A., & Wójtowicz, T. (2017). Spatial contagion between stock markets in Central Europe. Managerial Economics, 18(1), 23–44. https://doi.org/10.7494/manage.2017.18.1.23
Dedi, L., Yavas, B. F, & McMillan, D. (Reviewing Editor). (2016). Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies. Cogent Economics & Finance, 4(1), 1–18. https://doi.org/10.1080/23322039.2016.1266788
Drachal, K. (2017). Volatility clustering, leverage effects and risk-return tradeoff in the selected stock markets in the CEE countries. Romanian Journal of Economic Forecasting, 20(3), 37–53.
Duffee, G. (2005). Time variation in the covariance between stock returns and consumption growth. The Journal of Finance, 60(4), 1673–1712. https://doi.org/10.1111/j.1540-6261.2005.00777.x
Egert, B., & Kocenda, E. (2007). Interdependence between Eastern and Western European stock markets: Evidence from intraday data. Economic Systems, 31(2), 184–203. https://doi.org/10.1016/j.ecosys.2006.12.004
El Jebari, O., & Hakmaoui, A. (2018). GARCH Family Models vs EWMA: Which is the best model to forecast volatility of the Moroccan stock exchange market? Revista de Metodos Cuantitativos para la Economia Y La Empresa, 26, 237–249.
Engle, R. (2002). Dynamic conditional correlation. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
Forbes, K., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. Journal of Finance, 57(5), 2223–2261. https://doi.org/10.1111/0022-1082.00494
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Goetzmann, W. N., Li, L., & Rouwenhorst, K. G. (2005). Long-term global market correlations. The Journal of Business, 78(1), 1–38. https://doi.org/10.1086/426518
Hanousek, J., & Kočenda, E. (2011). Foreign news and spillovers in emerging European stock markets. The Review of International Economics, 19(1), 170–188. https://doi.org/10.1111/j.1467-9396.2010.00939.x
Harrison, B., & Moore, W. (2009). Spillover effects from London and Frankfurt to Central and Eastern European stock markets. Applied Financial Economics, 19(18), 1509–1521. https://doi.org/10.1080/09603100902902220
Hassan, M., Haque, M., & Lawrence, S. (2006). An empirical analysis of emerging stock markets of Europe. Quarterly Journal of Business and Economics, 45(1/2), 31–52.
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
King, M., Sentana, E., & Wadhwani, S. (1994). Volatility and links between national stock markets. Econometrica, 62(4), 901–933. https://doi.org/10.2307/2951737
Lane, D. (2007). Post-Communist States and the European Union. Journal of Communist Studies and Transition Politics, 23(4), 461–477. https://doi.org/10.1080/13523270701674558
Marquering, W. A., & de Goeij, P. (2002). Modelling the conditional covariance between stock and bond returns: A multivariate GARCH approach. ERIM Report Series No. ERS-2002-11-F&A; EFA 2002 Berlin Meetings Discussion Paper; EFMA 2002 London Meetings. https://doi.org/10.2139/ssrn.302335
Mehdiabadi, A., Tabatabeinasab, M., Spulbar, C., Karbassi Yazdi, A., & Birau, R. (2020). Are we ready for the challenge of Banks 4.0? Designing a roadmap for banking systems in Industry 4.0. International Journal of Financial Studies, 8(2), 32. https://doi.org/10.3390/ijfs8020032
Memon, B. A., Yao, H., Aslam, F., & Tahir, R. (2019). Network analysis of Pakistan stock market during the turbulence of economic crisis. Business, Management and Economics Engineering, 17(2), 269–285. https://doi.org/10.3846/bme.2019.11394
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Paczoski, A., Abebe, S. T., & Cirella, G. T. (2019). Debt and deficit growth rate reporting for postcommunist European Union Member States. Social Sciences, 8(6), 173. https://doi.org/10.3390/socsci8060173
Pagan, A., & Schwert, G. W. (1990). Alternative models for common stock volatilities. Journal of Econometrics, 45(1), 267–290. https://doi.org/10.1016/0304-4076(90)90101-X
Park, C. Y. & Mercado, R. (2014) Determinants of financial stress in emerging market economies. Journal of Banking & Finance, 45, 199–224. https://doi.org/10.1016/j.jbankfin.2013.09.018
Pinto, P., Hawaldar, I. T, Guruprasad, K., Rohit, B., Spulbar, C., Birau, R. & Stanciu, C. V. (2020). The impact of risk anomalies on the pharmaceutical sector of the Indian Stock Market – A comparative analysis between pharmaceutical, FMCG and IT companies. Revista de Chimie Journal, 71(2), 58–63. https://doi.org/10.37358/RC.20.2.7892
Rim, H., & Setaputra, R. (2018, May 15). Study on the co-movement between stock markets in Asia, Europe and the North America. In Proceedings of 35th International Academic Conference, Barcelona. IISES. https://doi.org/10.20472/IAC.2018.935.040
Savva, C. S., & Aslanidis, C. (2010). Stock market integration between new EU member states and the Eurozone. Empirical Economics, 39(2), 337–351. https://doi.org/10.1007/s00181-009-0306-6
Shakila, B., Prakash, P., Hawaldar, I. T., Spulbar, C., & Birau, R. (2020). The holiday effects in stock returns: A challenge for the textile and clothing industry of India. Industria Textila, 71(4), 327–333. https://doi.org/10.35530/IT.071.04.1710
Silvennoinen, A., & Teräsvirta, T. (2009). Multivariate GARCH models. In T. G. Andersen, R. A. Davis, J.-P. Kreib, & T. V. Mikosch (Eds.), Handbook of financial time series (SSE/EFI Working Paper Series in Economics and Finance No. 669) (pp. 201–229). Springer. https://doi.org/10.1007/978-3-540-71297-8_9
Spulbar, C., Trivedi, J., & Birau, R. (2020). Investigating abnormal volatility transmission patterns between emerging and developed stock markets: A case study. Journal of Business Economics and Management, 21(6), 1561–1592. https://doi.org/10.3846/jbem.2020.13507
Spulbar, C., & Birau, R. (2019). Emerging research on monetary policy, banking, and financial markets. IGI Global. https://doi.org/10.4018/978-1-5225-9269-3
Syriopoulos, T. (2007). Dynamic linkages between emerging European and developed stock markets: Has the EMU any impact? International Review of Financial Analysis, 16(1), 41−60. https://doi.org/10.1016/j.irfa.2005.02.003
Tilfani, O., Ferreira, P., & El Boukfaoui, M. Y. (2019). Revisiting stock market integration in Central and Eastern European stock markets with a dynamic analysis. Post-Communist Economies, 32(5), 643–674. https://doi.org/10.1080/14631377.2019.1678099
Tripathi, A. (2012). Study of correlation between selected Asian, European and American stock exchange market. SSRN. https://doi.org/10.2139/ssrn.2170019
Uğurlu, E. (2014). Modelling volatility: Evidence from the Bucharest stock exchange. Journal of Applied Economic Sciences (JAES), 9(4), 718–727.
Whitelaw, R. (1994). Time variations and covariations in the expectation and volatility of stock market returns. The Journal of Finance, 49(2), 515–541. https://doi.org/10.1111/j.1540-6261.1994.tb05150.x
Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/10.1016/0165-1889(94)90039-6
Živkov, D., Njegić, J., & Milenković, I. (2018). Interrelationship between DAX index and four largest Eastern European stock markets. Romanian Journal of Economic Forecasting, 21(3), 88–103.