Quantile connectedness amongst BRICS equity markets during the COVID-19 pandemic and Russia–Ukraine war

References

• Adrian, T., & Brunnermeier, M. (2016). CoVaR. American Economic Review, 106(7), 1705–19. https://doi.org/10.1257/aer.20120555
   Web of Science ®Google Scholar
• Agyei, S. K. (2023). Emerging markets equities’ response to geopolitical risk: Time-frequency evidence from the Russian-Ukrainian conflict era. Heliyon, 9(2), e13319. https://doi.org/10.1016/j.heliyon.2023.e13319
   PubMed Web of Science ®Google Scholar
• Agyei, S. K., Owusu, J. P., Bossman, A., Asafo-Adjei, E., Asiamah, O., & Adam, A. M. (2022). Spillovers and contagion between BRIC and G7 markets: New evidence from time-frequency analysis. PloS One, 17(7), e0271088. https://doi.org/10.1371/journal.pone.0271088
   PubMed Web of Science ®Google Scholar
• Ahmed, S., Hasan, M., & Kamal, M. (2022). Russia–Ukraine crisis: The effects on the European stock market. European Financial Management forthcoming, https://doi.org/10.1111/eufm.12386
   Web of Science ®Google Scholar
• Amar, A., Belaid, F., Youssef, A., & Guesmi, K. (2021). Connectedness among regional financial markets in the context of the COVID-19. Applied Economic Letters, 28(20), 1789–1796. https://doi.org/10.1080/13504851.2020.1854434
   Web of Science ®Google Scholar
• Ando, T., Greenwood-Nimmo, M., & Shin, Y. (2022). Quantile connectedness: Modelling tail behaviour in the topology of financial markets. Management Sciences, 68(4), 2401–2431. https://doi.org/10.1287/mnsc.2021.3984
   Web of Science ®Google Scholar
• Antipova, T. (2021). Coronavirus Pandemic as Black Swan Event. In T. Antipova (Ed.), Integrated Science in Digital Age 2020. ICIS 2020. Lecture Notes in Networks and Systems (Vol. 136). Springer. https://doi.org/10.1007/978-3-030-49264-9_32
   Google Scholar
• An, H., Wang, H., Delpachitra, S., Cottrell, S., & Yu, X. (2022). Early warning system for risk of external liquidity shock in BRICS countries. Emerging Markets Review, 51(A), e100878. https://doi.org/10.1016/j.ememar.2021.100878
   Google Scholar
• Asafo-Adjei, E., Bossman, A., Boateng, E., Owusu, J. P., Idun, A. A., Agyei, S. K., Adam, A. M., & Li, Y. (2022). A nonlinear approach to quantifying investor fear in stock markets of BRIC. Mathematical Problems in Engineering, 2022, 1–20. https://doi.org/10.1155/2022/9296973
   Web of Science ®Google Scholar
• Bae, K., Karolyi, G., & Stulz, R. (2003). A new approach to measuring financial contagion. Review of Financial Studies, 16(3), 717–763. https://doi.org/10.1093/rfs/hhg012
   Web of Science ®Google Scholar
• Beine, M., Cosma, A., & Vermeulen, R. (2010). The dark side of global interaction: Increasing tail dependence. Journal of Banking and Finance, 34(1), 184–192. https://doi.org/10.1016/j.jbankfin.2009.07.014
   Web of Science ®Google Scholar
• Bhar, R., & Nikolova, B. (2009). Return, volatility spillovers and dynamic correlation in the BRIC equity markets: An analysis using a bivariate EGARCH framework. Global Finance Journal, 19(3), 203–218. https://doi.org/10.1016/j.gfj.2008.09.005
   Google Scholar
• Billah, M., Karim, S., Naeem, M., & Vigne, S. (2022). Return and volatility spillovers between energy and BRIC markets: Evidence from quantile connectedness. Research in International Business and Finance, 62, e101680.
   Web of Science ®Google Scholar
• Bollerslev, T., Chou, R., & Kroner, K. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52(1–2), 5–59.
   Web of Science ®Google Scholar
• Bonga-Bonga, L. (2017). Assessing the readiness of the BRICS grouping for mutually beneficial financial integration. Review of Development Economics, 21(4), 204–219. https://doi.org/10.1111/rode.12324
   Web of Science ®Google Scholar
• Bonga-Bonga, L. (2018). Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model. The Quarterly Review of Economics & Finance, 67, 36–44. https://doi.org/10.1016/j.qref.2017.04.009
   Web of Science ®Google Scholar
• Bossman, A., & Gubareva, M. (2023). Asymmetric impacts of geopolitical risk on stock markets: A comparative analysis of the E7 and G7 equities during the Russian-Ukrainian conflict. Heliyon, 9(2), e13626. https://doi.org/10.1016/j.heliyon.2023.e13626
   PubMed Web of Science ®Google Scholar
• Bossman, A., Owusu, P., & Tiwari, A. (2022). Dynamic connectedness and spillovers between Islamic and conventional stock markets: Time- and frequency-domain approach in COVID-19 era. Heliyon, 8(4), e09215. https://doi.org/10.1016/j.heliyon.2022.e09215
   PubMed Web of Science ®Google Scholar
• Bossman, A., Teplova, T., Umar, Z., & Wang, G. J. (2022). Do local and world COVID-19 media coverage drive stock markets? Time-frequency analysis of BRICS. Complexity, 2022, 1–14. https://doi.org/10.1155/2022/2249581
   Web of Science ®Google Scholar
• Boubaker, S., Goodell, J., Pandey, D., & Kumari, V. (2022). Heterogenous impacts of wars on global equity markets: Evidence from the invasion of Ukraine. Finance Research Letters, 48, e102934. https://doi.org/10.1016/j.frl.2022.102934
   Web of Science ®Google Scholar
• Boungou, W., & Yatie, A. (2022). The impact of the Ukraine-Russia war on the world stock market returns. Economics Letters, 215, e110516. https://doi.org/10.1016/j.econlet.2022.110516
   Web of Science ®Google Scholar
• Bouri, E., Saeed, T., Vo, X., & Roubaud, D. (2021). Quantile connectedness in the cryptocurrency market. Journal of International Financial Markets, Institutions and Money, 71, e101302. https://doi.org/10.1016/j.intfin.2021.101302
   Web of Science ®Google Scholar
• Buar, D., & Schulze, N. (2005). Coexceedances in financial markets—A quantile regression analysis of contagion. Emerging Markets Review, 6(1), 21–43. https://doi.org/10.1016/j.ememar.2004.10.001
   Google Scholar
• Cappiello, L., Gerad, B., Kadareja, A., & Manganelli, S. (2006). Measuring comovements by regression quantiles. Journal of Financial Econometrics, 12(4), 645–678. https://doi.org/10.1093/jjfinec/nbu009
   Google Scholar
• Chan-Lau, J., Mathieson, D., & Yao, J. (2004). Extreme contagion in equity markets. SSRN Electronic Journal, 386–408. https://doi.org/10.2139/ssrn.382502
   Google Scholar
• Chen, S. (2022). Research on spillover effect of financial market based on TVP-VAR model – evidence from the BRICS stock markets. World Scientific Research Journal, 3(1), 174–182. https://doi.org/10.6911/WSRJ.202203_8(3).0022
   Google Scholar
• Christian, C., & Ranaldo, A. (2009). Extreme coexceedances in new EU member states’ stock markets. Journal of Banking and Finance, 33(6), 1048–1057. https://doi.org/10.1016/j.jbankfin.2008.10.014
   Web of Science ®Google Scholar
• Costa, A., Matos, P., & da Silva, C. (2022). Sectoral connectedness: New evidence from US stock market during the COVID-19 pandemics. Finance Research Letters, 45, e102124. https://doi.org/10.1016/j.frl.2021.102124
   PubMed Web of Science ®Google Scholar
• Diebold, F., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with an application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
   Web of Science ®Google Scholar
• Diebold, F., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International journal of forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
   Web of Science ®Google Scholar
• Diebold, F., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119–134.
   Web of Science ®Google Scholar
• Engle, R., & Manganelli, S. (2004). CaViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business and Economic Statistics, 22(4), 367–381. https://doi.org/10.1198/073500104000000370
   Web of Science ®Google Scholar
• Federle, J., Meier, A., Muller, G., & Sehn, V. (2022). Proximity to war: The stock market response to the Russian invasion of Ukraine. CEPR Discussion Paper No. DP17185.
   Google Scholar
• Forbes, K., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance, 57(5), 2223–2261. https://doi.org/10.1111/0022-1082.00494
   Web of Science ®Google Scholar
• Glambosky, M., & Peterburgsky, S. (2022). Corporate activism during the 2022 Russian invasion of Ukraine. Economic Letters, 217, e110650. https://doi.org/10.1016/j.econlet.2022.110650
   Web of Science ®Google Scholar
• Hardle, W., Wang, W., & Yu, L. (2016). TENET: Tail-event driven network risk. Journal of Econometrics, 192(2), 499–513. https://doi.org/10.1016/j.jeconom.2016.02.013
   Web of Science ®Google Scholar
• Hautsch, N., Schaumburg, J., & Schienle, M. (2015). Financial network systematic risk contributions. Review of Finance, 19(2), 685–738. https://doi.org/10.1093/rof/rfu010
   Web of Science ®Google Scholar
• Jena, S., Tiwari, A., Abakah, E., & Hammoudeh, S. (2022). The connectedness in the world petroleum futures markets using a quantile VAR approach. Journal of Commodity Markets, 27, e100222. https://doi.org/10.1016/j.jcomm.2021.100222
   Web of Science ®Google Scholar
• Koop, G., Pesaran, H., & Potter, S. (1996). Impulse response analysis in nonlinear multivariate models. Journal of econometrics, 74(1), 119–147.
   Web of Science ®Google Scholar
• Koyuncu, M. (2022). The 14th BRICS summit: Did pandora’s box open? Ankara center for crisis and policy studies. Retrieved July 4, 2022, from https://www.ankasam.org/the-14th-brics-summit-did-pandoras-boxopen/?lang=en.
   Google Scholar
• Liu, Z., Shi, X., Zhai, P., Wu, S., Ding, Z., & Zhou, Z. (2021). Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach. Resources Policy, 74, e102381. https://doi.org/10.1016/j.resourpol.2021.102381
   Web of Science ®Google Scholar
• Lui, Y., Wei, Y., & Lui, Y. (2022). International stock market risk contagion during the COVID-19 pandemic. Finance Research Letters, 45, e102145. https://doi.org/10.1016/j.frl.2021.102145
   PubMed Web of Science ®Google Scholar
• Malik, K., Sharma, S., & Kaur, M. (2022). Measuring contagion during the COVID-19 through volatility spillovers of BRIC countries using diagonal BEKK approach. Journal of Economic Studies, 49(2), 227–242. https://doi.org/10.1108/JES-05-2020-0246
   Web of Science ®Google Scholar
• Markwat, T., Kole, E., & van Djik, D. (2009). Contagion as a domino effect in global stock markets. Journal of Banking and Finance, 33(11), 1996–2012. https://doi.org/10.1016/j.jbankfin.2009.05.008
   Web of Science ®Google Scholar
• Mati, M., Vukovic, D., Vyklyuk, Y., & Grubisic, Z. (2022). BRICS capital markets co-movement analysis and forecasting. Risks, 10(5), 88. https://doi.org/10.3390/risks10050088
   Web of Science ®Google Scholar
• Mensi, W., Lee, Y., Vo, W., & Yoon, S. (2021). Quantile connectedness among gold, gold mining, silver, oil and energy sector uncertainty indexes. Resources Policy, 74, e102450. https://doi.org/10.1016/j.resourpol.2021.102450
   Web of Science ®Google Scholar
• Mohamad, A. (2022). Safe flight to which haven when Russia invades Ukraine? A 48-hour story. Economics Letters, 216, e110558. https://doi.org/10.1016/j.econlet.2022.110558
   Web of Science ®Google Scholar
• Naeem, M., Farid, S., Arif, M., Paltrinieri, A., & Alharthi, M. (2022). COVID-19 and connectedness between sustainable and Islamic equity markets. Borsa Instabul Review, 23(1), 1–21. (forthcoming). https://doi.org/10.1016/j.bir.2022.06.001
   Web of Science ®Google Scholar
• Ngwakwe, N. (2022). Stock market volatility during rumours of war and actual war: Case of Russia-Ukraine conflict. Acta Universitatis Danubius OEconomica, 18(3), 55–70. https://dj.univ-danubius.ro/index.php/AUDOE/article/view/1727
   Google Scholar
• Ozkan, O. (2021). Impact of COVID-19 on stock market efficiency: Evidence from developed countries. Research in International Business and Finance, 58, e101445. https://doi.org/10.1016/j.ribaf.2021.101445
   PubMed Web of Science ®Google Scholar
• Panda, P., & Thiripalraju, M. (2018). Return and volatility spillover amongst stock markets: BRICS countries experience. Afro-Asian Journal of Finance and Accounting, 8(2), 148–166. https://doi.org/10.1504/AAJFA.2018.091057
   Google Scholar
• Pesaran, H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economic Letters, 58(1), 17–29.
   Web of Science ®Google Scholar
• Qian, Y., Hardle, W., & Chen, C. (2020). Modelling industry interdependency dynamics in a network context. Studies in Economics and Finance, 37(1), 50–70. https://doi.org/10.1108/SEF-07-2019-0272
   Web of Science ®Google Scholar
• Rehman, M., Ahmad, N., Shahzad, S., & Vo, X. (2022). Dependence dynamics of stock markets during COVID-19. Emerging Markets Review, 51(B), e100894. https://doi.org/10.1016/j.ememar.2022.100894
   Google Scholar
• Salisu, A., Gupta, R., & Pierdzioch, C. (2022). Predictability of tail risks of Canada and the U.S. over a century: The role of spillovers and oil tail risks. The North American Journal of Economics & Finance, 59, e101620. https://doi.org/10.1016/j.najef.2021.101620
   Web of Science ®Google Scholar
• Shahzad, S., Rehman, M., & Jammazi, R. (2019). Spillovers from oil to precious metals: Quantile approaches. Resources Policy, 61, 508–521. https://doi.org/10.1016/j.resourpol.2018.05.002
   Web of Science ®Google Scholar
• Shi, K. (2021). Spillovers of stock markets among the BRICS: New evidence in time and frequency domains before the outbreak of COVID-19 pandemic. Journal of Risk and Financial Management, 14(3), 112. https://doi.org/10.3390/jrfm14030112
   Web of Science ®Google Scholar
• Siddiqui, A., Shamim, M., Asif, M., & Al-Faryan, M. (2022). Are stock markets among BRICS members integrated? A regime shift-based co-integration analysis. Economies, 10(4), 87. https://doi.org/10.3390/economies10040087
   Web of Science ®Google Scholar
• Sun, M., & Zhang, C. (2022). Comprehensive analysis of global stock market reactions to the Russia-Ukraine war. Applied Economic Letters, 1–8. (forthcoming). https://doi.org/10.1080/13504851.2022.2103077
   Web of Science ®Google Scholar
• Taleb, N. (2005, March). Fat tails, asymmetric knowledge, and decision making: Essay in honor of Benoit Mandelbrot’s 80th birthday. Technical Paper Series, 56–59. Wimott.
   Google Scholar
• Taleb, N. (2007). The black swan: The impact of the highly improbable. Random House.
   Google Scholar
• Tiwari, A., Abakah, E., Adewuyi, A., & Lee, C. (2022). Quantile risk spillovers between energy and commodity markets: Evidence from pre and during COVID-19 pandemic. Energy Economics, 113, e106235. https://doi.org/10.1016/j.eneco.2022.106235
   Web of Science ®Google Scholar
• Umar, Z., Bossman, A., Choi, S., & Vo, X. (2022). Are short stocks susceptible to geopolitical shocks? time-frequency evidence from the Russian-Ukraine conflict. Finance Research Letters 52, 103388. (forthcoming). https://doi.org/10.1016/j.frl.2022.103388
   Web of Science ®Google Scholar
• Urom, C., Ndubuisi, G., Lo, G., & Yuni, D. (2022). Global commodity and equity market spillovers to Africa during COVID-19 pandemic. Emerging Markets Review 55, 100948. (forthcoming). https://doi.org/10.1016/j.ememar.2022.100948
   Web of Science ®Google Scholar
• Vo, D., Ho, C., & Dang, T. (2022). Stock market volatility from the COVID-19 pandemic: New evidence from the Asia-Pacific region. Heliyon, 8(9), e10763. https://doi.org/10.1016/j.heliyon.2022.e10763
   PubMed Web of Science ®Google Scholar
• White, H., Kim, T., & And Manganelli, S. (2015). VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics, 187(1), 169–188. https://doi.org/10.1016/j.jeconom.2015.02.004
   Web of Science ®Google Scholar
• Yousaf, I., Patel, R., & Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia-Ukraine conflict “Black-swan” event: Evidence from event study approach. Journal of Behavioural and Experimental Finance, 35, e100723. https://doi.org/10.1016/j.jbef.2022.100723
   Web of Science ®Google Scholar
• Yu, Z., Bai, L., & Li, Y. (2021). Analysis of integration and dynamic linkages of BRICS stock markets. Advances in Economics, Business and Management Research, 166, 755–761. https://doi.org/10.2991/aebmr.k.210319.140
   Google Scholar
• Yu, H., Chu, W., Ding, Y., & Zhao, X. (2021). Risk contagion of global stock markets under COVID-19: A network connectedness approach. Accounting and Finance, 61(4), 5745–5782. https://doi.org/10.1111/acfi.12775
   Web of Science ®Google Scholar
• Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, e101528. https://doi.org/10.1016/j.frl.2020.101528
   PubMed Web of Science ®Google Scholar
• Zhang, W., Zhuang, X., Wang, J., & Lu, Y. (2020). Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network. The North American Journal of Economics & Finance, 54, e101248. https://doi.org/10.1016/j.najef.2020.101248
   Web of Science ®Google Scholar
• Zhu, X., Wang, W., Wang, H., & Hardle, W. (2019). Network quantile regression. Journal of Econometrics, 212(1), 345–358. https://doi.org/10.1016/j.jeconom.2019.04.034
   Web of Science ®Google Scholar