Investigating abnormal volatility transmission patterns between emerging and developed stock markets: a case study
Abstract
The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.
Keyword : volatility spillovers, developed stock markets, emerging stock markets, GARCH models, correlation
How to Cite
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Beirne, J., Caporale, G. M., Schulze‐Ghattas, M., & Spagnolo, N. (2013). Volatility spillovers and contagion from mature to emerging stock markets. Review of International Economics, 21(5), 1060–1075. https://doi.org/10.1111/roie.12091
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Chang, C. L., & McAleer, M. (2017). The correct regulatory condition and interpretation of asymmetry in EGARCH. Economics Letters, 161(C), 52–55. https://doi.org/10.1016/j.econlet.2017.09.017
Chiang, T. C., Bang, N. J., & Huimin, L. (2007). Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money Finance, 26(7), 1206–1228. https://doi.org/10.1016/j.jimonfin.2007.06.005
Chitkasame, T., & Tansuchat, R. (2019). An analysis of contagion effect on ASEAN stock market using multivariate Markov switching DCC GARCH. Thai Journal of Mathematics (Special Issue: Structural Change Modeling and Optimization in Econometrics), 135–152.
Choudhry, T. (1996). Stock market volatility and the crash of 1987: Evidence from six emerging markets. Journal of International Money and Finance, 15(6), 969–981. https://doi.org/10.1016/S0261-5606(96)00036-8
Chowdhury, A. H., & Arefin, M. K. (2017). Modelling co-movement of different sectors in Dhaka Stock Exchange (DSE) using asymmetric BVAR-GARCH models. Journal of Finance and Economics, 5(3), 105–117. https://doi.org/10.12691/jfe-5-3-3
Chua, C. L., & Tsiaplias, S. (2019). Information flows and stock market volatility. Journal of Applied Econometrics, 34(1), 129–148. https://doi.org/10.1002/jae.2649
Dedi, L., Yavas, B. F., & McMillan, D. (2016). Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies. Cogent Economics & Finance, 4(1), 1266788. https://doi.org/10.1080/23322039.2016.1266788
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Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: Understanding how it spreads. The World Bank Research Observer, 15(2), 177–197. https://doi.org/10.1093/wbro/15.2.177
Dutta, P., Noor, M. H., & Dutta, A. (2017). Impact of oil volatility shocks on global emerging market stock returns. International Journal of Managerial Finance, 13(5), 578–591. https://doi.org/10.1108/IJMF-03-2017-0039
Dutta, A. (2014). Modelling volatility: Symmetric or asymmetric GARCH models? Journal of Statistics: Advances in Theory and Applications, 12(2), 99–108.
Engle, R., Ito, T., & Lin, W. (1990). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica, 58(3), 525–542. https://doi.org/10.2307/2938189
Engle, R. F. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157–168. https://doi.org/10.1257/jep.15.4.157
Fasanya, I. O., & Akinde, M. A. (2019). Volatility transmission in the Nigerian financial market. The Journal of Finance and Data Science, 5(2), 99–115. https://doi.org/10.1016/j.jfds.2019.01.003
Fleming, J., Kirby, C., & Ostdiek, B. (1998). Information and volatility linkages in the stock, bond, and money markets. Journal of Financial Economics, 49(1), 111–137. https://doi.org/10.1016/S0304-405X(98)00019-1
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