Measuring contagion during COVID-19 through volatility spillovers of BRIC countries using diagonal BEKK approach

Abstract

PurposeThe outbreak of the coronavirus disease 2019 (COVID-19) pandemic is an unprecedented shock to the BRICS (Brazil, Russia, India, China, South Africa) economy and their financial markets have plummeted significantly due to it. This paper adds to the recent literature on contagion due to spillover by uniquely examining the presence of pairwise contagion or volatility transmissions in stock markets returns of India, Brazil, Russia, China and USA prior to and during COVID-19 pandemic period.Design/methodology/approachIn this study, the generalised autoregressive conditional heteroskedasticity (GARCH) by Bollerslev (1986) under diagonal parameterization is used to estimate multivariate GARCH framework also known as BEKK (Baba EngleKraft and Kroner) model on stock market returns of BRIC nations and the US.FindingsThe empirical results show that the model captures the volatility spillovers and display statistical significance for own past mean and volatility with both short- and long-run persistence effects. Own volatility spillovers (Heatwave phenomenon) have been found to be highest for the US, China and Brazil compared to Russia and India. The coefficients indicate persistence of volatility for each country in terms of its own past errors. The highest and long-term spillover effect is found between US and Russia. The results recommend that Russia is least vulnerable to outside shocks. Finally after examining the pairwise results, it is suggested that the BRIC countries stock indices have exhibited volatility spillover due to the COVID-19 pandemic.Research limitations/implicationsThe study may be extended to include other emerging market economies under a dynamic framework.Practical implicationsResearchers and policymakers may draw useful insights on cross-market interdependencies regarding the spillovers in BRIC countries' stock markets. It also helps design international portfolio diversification strategies and in constructing optimal portfolios during COVID and in a post-COVID world.Originality/valueCOVID-19 has been an improbable event in the history of the world which can have a large impact on the financial economies across the emerging countries. This event can be deemed to be informative enough to measure the co-movements of the equity markets amongst cross-country return series, which has not been investigated so far for BRIC nations.

COVID-19 as Information Transmitter to Global Equity Markets: Evidence from CEEMDAN-Based Transfer Entropy Approach

“…On the other hand, many studies on the impact of COVID-19 on equities have focused mainly on the quantum of spillover between markets (see, e.g., [18,[39][40][41][42][43][44][45]). However, it is evident that, under the circumstances, the driving force of these transmissions is panic or fear from the pandemic.…”

COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

“…Accordingly, in the current investigation, all the explored variables register a value of kurtosis greater than three. This fact indicates that the return series has fatter tails than the normal distribution, similar to Banerjee (2021), Bourghelle et al (2021), Fakhfekh et al (2021), Ftiti et al (2021, Malik et al (2021), Yu et al (2021), and Zhang and Hamori (2021). This feature is referred to as leptokurtosis, which could be caused by volatility clustering.…”

“…Table 3 shows descriptive statistics for the daily logarithmic returns of the shares traded on BSE, as well as for the BET stock market index, whereas Figure 1 reveals the density graphs. The selected shares have a negative skewness (except for the TRP share) in line with Agarwalla et al (2021), Banerjee (2021), Malik et al (2021), Yousaf (2021), and Zhang and Hamori (2021). As a common condition of financial markets, skewness is negative, suggesting an asymmetry to the left.…”

“…Figure 3 shows the evolution of the selected returns. Hence, there is acknowledged a phenomenon of "volatility clustering" and an alternation between periods of low volatility and those with high volatility, similar to Abuzayed et al (2021), Insaidoo et al (2021), Malik et al (2021), andYousfi et al (2021). Moreover, "volatility clustering" implies a strong autocorrelation of returns.…”

“…;Ghorbel and Jeribi 2021;Harjoto and Rossi 2021;Haroon and Rizvi 2020;Hongsakulvasu et al 2020;Insaidoo et al 2021;Le and Tran 2021;Liu et al 2021b;Malik et al 2021;Mariana et al 2021; Omane-Adjepong and Alagidede 2021;Szczygielski et al 2021aSzczygielski et al , 2021bUddin et al 2021; Vera-Valdés 2021;Xu 2021;Yousaf 2021;Yousfi et al 2021;Yu et al 2021;Zhang and Hamori 2021;Zoungrana et al 2021). These models simultaneously evaluate and test processes of yields and volatility processes.…”