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

Figures & data

Table 1. Descriptive statistics

Panel A: Returns	Brazil	Russia	India	China	South Africa
Mean	0.040	−0.654	0.068	0.037	0.077
Std. Dev.	1.965	3.918	1.561	1.142	1.515
Skewness	−1.297	−3.647	−0.775	−0.058	−0.681
Kurtosis	20.381	17.811	12.233	4.793	10.363
Jarque-Bera	7746.688	6836.606	2198.435	80.978	1406.470
ADF test	−31.123	−4.263	−25.470	−15.198	−16.731
PP test	−30.565	−10.345	−25.449	−24.962	−24.009
LQ(10)	23.119***	121.920***	31.580***	20.603**	24.902***
LQ-squared (10)	362.020***	112.150***	300.010***	125.120***	542.32***
ARCH(10)	332.698***	60.501***	290.997***	57.013***	173.644***
Panel B: Volatility
Mean	3.881	14.263	2.567	1.277	2.137
Std. Dev.	11.497	51.710	4.967	0.992	3.501
Skewness	6.771	4.721	5.017	4.133	6.147
Kurtosis	51.879	24.367	29.312	25.169	43.510
Jarque-Bera	64420	13665	19858	14018	44881
ADF test	−47.310	−4.220	−12.513	−8.286	−10.929
PP test	−49.153	−3.465	−16.885	−8.032	−34.031
LB (10)	7.080	14.170	5.924	15.819	11.914
LB-squared (10)	4.100	3.178	11.681	9.260	13.081
ARCH(10)	4.088	3.157	11.193	7.594	10.786

Notes: ***, ** and * indicate significant at 1%, 5%, and 10% respectively. The ADF and PP tests is the augmented Dickey–Fuller test and Phillips–Perron test for unit root. The critical values at 1%, 5% and 10% are −3.441, −2.866 and −2.569 respectively. ARCH(10) indicates the autoregressive conditional heteroscedasticity test at lag ten. Lastly, LB and LB-squared are the Ljung-Box statistics for serial correlation in the return (volatility) series and squared return (volatility) series, respectively.

Figure 1. Evolution of return and volatility.

Table 2. Static connectedness and spillover (return)

Return	Brazil	Russia	India	China	South Africa	From
Lower (left tail = 0.05)
Brazil	27.90	17.49	19.20	17.62	17.80	72.10
Russia	17.56	30.70	18.71	15.34	17.70	69.30
India	18.26	18.49	27.72	16.85	18.68	72.28
China	18.67	15.55	17.85	29.81	18.12	70.19
South Africa	18.71	18.82	18.63	17.13	26.71	73.29
To	73.19	70.34	74.39	66.94	72.30	TCI = 71.43
Include Own	101.09	101.04	102.11	96.75	99.01
Net	1.09	1.04	2.11	−3.25	−0.99
Median (0.50)
Brazil	72.99	5.39	8.85	6.00	6.78	27.01
Russia	5.17	75.64	9.67	1.65	7.87	24.36
India	8.87	10.77	70.57	3.30	6.50	29.43
China	6.89	2.32	3.90	80.18	6.71	19.82
South Africa	7.62	9.17	7.36	5.09	70.75	29.25
To	28.56	27.65	29.78	16.03	27.85	TCI = 25.97
Include Own	101.55	103.29	100.35	96.21	98.61
Net	1.55	3.29	0.35	−3.79	−1.39
Upper (right tail = 0.95)
Brazil	30.07	16.45	17.90	17.54	18.04	69.93
Russia	16.98	31.29	18.78	15.07	17.88	68.71
India	18.01	18.21	29.24	16.25	18.28	70.76
China	18.22	14.66	16.72	32.08	18.32	67.92
South Africa	17.46	17.71	18.11	17.32	29.40	70.60
To	70.67	67.04	71.52	66.18	72.51	TCI = 69.58
Include Own	100.74	98.32	100.76	98.26	101.91
Net	0.74	−1.68	0.76	−1.74	1.91

Notes: TCI indicates the total connectedness index.

Table 3. Static connectedness and spillover (volatility)

Volatility	Brazil	Russia	India	China	South Africa	From
Lower (left tail = 0.05)
Brazil	61.36	7.95	8.96	9.58	12.15	38.64
Russia	8.21	64.57	12.19	5.79	9.23	35.43
India	9.58	12.82	64.92	4.05	8.63	35.08
China	9.79	6.14	4.31	70.98	8.78	29.02
South Africa	12.13	9.17	8.26	8.15	62.29	37.71
To	39.71	36.08	33.73	27.56	38.79	TCI = 35.17
Include Own	101.07	100.65	98.64	98.55	101.08
Net	1.07	0.65	−1.36	−1.45	1.08
Median (0.50)
Brazil	70.68	4.90	4.87	7.24	12.31	29.32
Russia	3.78	80.13	8.47	2.57	5.05	19.87
India	3.87	12.74	76.89	0.98	5.53	23.11
China	4.33	1.54	3.34	82.19	8.59	17.81
South Africa	7.48	4.57	6.54	7.17	74.24	25.76
To	19.46	23.75	23.22	17.95	31.48	TCI = 23.17
Include Own	90.14	103.89	100.11	100.14	105.73
Net	−9.86	3.89	0.11	0.14	5.73
Upper (right = 0.95)
Brazil	19.74	18.63	14.87	28.59	18.17	80.26
Russia	17.32	20.53	15.13	28.75	18.26	79.47
India	16.73	19.89	17.91	27.02	18.45	82.09
China	17.02	17.49	15.06	32.59	17.84	67.41
South Africa	17.16	19.51	14.54	29.97	18.83	81.17
To	68.22	75.52	59.60	114.33	72.73	TCI = 78.08
Include Own	87.96	96.05	77.51	146.92	91.56
Net	−12.04	−3.95	−22.49	46.92	−8.44

Notes: TCI indicates the total connectedness index.

Figure 2. Total connectedness index.

Figure 3. Net directional spillover.

Figure 3. (Continued).

Table A1. Review of previous studies on BRICS equity market spillovers

Authors	Countries	Period	Methods	Results
Agyei (2023)	BRIC + Mexico, Turkey and Indonesia	February 2022 – July 2022	Wavelet and TVP-VAR	Asymmetric market-specific coherence and lead-lag pattern with Brazil, China and Russia as the net receivers while India is a net transmitter.
Agyei et al. (2022)	BRIC and G7	2012 – 2021	Barunik and Krehlik spillover index	High connectedness among BRIC with Brazil as the net transmitter while others are net receivers from the G7 market.
Asafo-Adjei et al. (2022)	BRIC	2012 – 2021	Variational mode of decomposition and Transfer entropy	Negative information spillover during the stress and normal conditions while positive information spillover was documented during boom condition.
Bhar and Nikolova (2009)	BRIC	January 1995 – October 2005	EGARCH	BRIC are influenced by others except Russia who is main transmitter/highest integration for India followed by Brazil and Russia whilst China no evidence of integration.
Bonga-Bonga (2017)	BRICS	January 1998 – February 2017Returns and volatility Returns and volatility	D-Y index and FIGARCH model	SA, India and [Brazil] are main receiver whilst Russia and China are the main transmitters of shocks
Bonga-Bonga (2018)	BRICS	December 1996 – May 2012	Multivariate GARCH/VAR-DCC-GARCH	SA more affected by crisis originating from BRIC but these countries are not affected by crisis originating from SA
Bossman et al. (2022)	BRICS	January 2020 – March 2022	Squared wavelet Coherence and wavelet phase difference	The leading role of the BRICS equity market was documented during the initial outbreak. Also, media coverage had significant effect on the BRICS equities.
Panda and Thiripalraju (2018)	BRICS	26 June 2022–31 July 2014	EGARCH	Bidirectional and unidirectional spillover amongst the BRICS countries
Chen (2022)	BRICS	2002–2020	TVP-VAR	China, India and Brazil are net transmitters of volatility spillovers whilst Russia and SA are net receivers.
Malik et al. (2022)	BRIC	1 January 2013–24 April 2020	GARCH-BEKK	Pre-COVID: China and Brazil more affected by own shocks while Russia and India are more vulnerable to outside shocks During-COVID: volatility spillovers greatest for Brazil and last for China
Mati et al. (2022)	BRICS	10 December 1999–17 January 2019	Wavelet coherence, wavelet clustering and artificial neural network forecasting	Contagion effects amongst BRICS countries more prominent at high frequencies or over the short-run
Shi (2021)	BRICS	1 August 2002–31 December 2019	TVP-VAR	Russia, India and China are net receivers’ transmitters of shocks across time and frequency
Siddiqui et al. (2022)	BRICS	1 January 2004–31 December 2018	Structural break cointegration tests	No cointegration between Brazil-China, India-China and China-SA
Yu et al. (2021)	BRICS	22 May 2012–31 August 2020	VAR model	China is influenced by other countries but does not affect other. BRIS countries have bi-directional spillover effects.

Table A2. Robustness test for the GARCH model

Parameters	Brazil	Russia	India	China	South Africa
Panel A: Mean equation
μ	0.0403	0.0904**	0.1032**	0.0415	0.0769
θ	−0.0816**	0.0988**	−0.0351	−0.0249	0.0907**
Panel B: Variance equation
ω	0.1367***	0.0538*	0.0509***	0.1645**	0.1756***
α	0.0427**	0.2258***	0.0600**	0.1860***	0.1213***
ρ	0.8769***	0.7784***	0.9006***	0.6739***	0.7630***
Panel C: Diagnostic tests
LB(10)	7.0796	14.170	5.9235	15.819	11.914
LB-squared (10)	4.1000	3.1778	11.681	9.2596	13.081
ARCH(10)	4.0878	3.1571	11.193	7.5936	10.782

Notes: ***, ** and * indicate significant at 1%, 5% and 10%, respectively. LB is the Ljung-Box statistics, LB-squared is Ljung-Box squared, and ARCH test is the autoregressive conditional heteroskedasticity test.

Table A3. Zivot-Andrew structural break unit root test result

Market	Returns		Volatility
	Test statistic	Break point	Test statistic	Break point
Brazil	−15.528**	10/01/2022	−25.731**	26/08/2021
Russia	−7.5363***	17/02/2022	−10.117***	23/02/2022
India	−10.874	20/10/2021	−11.731***	21/01/2022
China	−15.413*	19/02/2021	−9.5007**	24/02/2022
South Africa	−24.038*	22/09/2021	−11.696	23/02/2022

Notes: ***, ** and * indicate significant at 1%, 5% and 10%, respectively.

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

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

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

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., 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

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

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

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

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

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

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