Member states’ pact and industry co-movements in the BRICS markets

ABSTRACT

Through the existence of supply chain relationships among BRICS (Brazil, Russia, India, China and South Africa), we explore the industry return co-movements of the BRICS markets and the impacts of BRICS-related events on the time-varying conditional correlation and volatility. We find that BRICS-related events have increased industry co-movements and substantially reduced volatility. An asymmetry in industry return co-movements shows a strong response to good news. The first formal BRICS summit in 2009 is the most dominant event influencing BRICS industry co-movements, while there is a significant decline in correlations during the 2013 BRICS event. The financial industries of BRICS have the highest co-movements among sampled industries.

KEYWORDS:

• Industry
• co-movements
• BRICS
• dynamic conditional correlation

JEL CLASSIFICATION:

• F30
• G15
• E44
• C32

Acknowledgement

We would like to thank the Editor and the two anonymous referees for their highly constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ China is the main supply partner to Russia and India and only second to Brazil. China is also an important customer, representing the third largest market for Brazil, fourth for India and sixth for Russia (Biggemann and Fam 2011). See Biggemann and Fam (2011) and Filippetti and Peyrache (2011) for more detail.

² Naranjo and Porter (2010) study whether the industry factor is the cross-country co-movement of momentum returns. We define industry co-movement as the co-movement across industries in stock returns.

³ The Heads of BRICS declared this at the BRICS leaders third summit meeting on 14 April 2011. Source: www.brics5.co.za/about/summit-declaration/third-summit/.

⁴ We omit the figures of the other seven industry returns for brevity, but they are available upon request.

⁵ We remove the data series of INDU of Russia, which are stable for the sample period, from analysis.

⁶ The absence of an ARCH effect is rejected uniformly.

⁷ The GARCH model: ${\sigma }_t^2=\omega +\alpha {\epsilon }_{t-1}^2+\beta {\sigma }_{t-1}^2$. The EGARCH (exponential GARCH) model of Nelson (1991) is formulated in terms of the logarithm of the conditional variance, as in the EGARCH(1,1) model: $ln\left({\sigma }_t^2\right)=\omega +\alpha Z_{t-1}^{\hspace{0.17em}}+\gamma \left(\left|Z_{t-1}\right|-E\left|Z_{t-1}\right|\right)+\beta ln\left({\sigma }_{t-1}^2\right)$. The parametrization in terms of logarithms has the obvious advantage of avoiding non-negativity constraints on the parameters. Here, $Z_{t-1}^{\hspace{0.17em}}$ is the standardized innovation. The GJR-GARCH defined by Glosten, Jagannathan, and Runkle (1993) and Zakoian (1994) augment the GARCH model by including an additional ARCH term conditional on the sign of the past innovation. GJR-GARCH model: ${\sigma }_t^2=\omega +\alpha {\epsilon }_{t-1}^2+\gamma I_{t-1}{\epsilon }_{t-1}^2+\beta {\sigma }_{t-1}^2$, where $I=\left\{\begin{array}{c}\begin{array}{cc}1,& \epsilon \le 0\end{array}\\ \begin{array}{cc}0,& \mathrm{o}.\mathrm{w}.\end{array}\end{array}\right. .$

⁸ We omit the figures of dynamic conditional correlation in the other seven industry returns for brevity, but they are available upon request.