Herding and BRICS markets: a study of distribution tails

Abstract

Purpose

The present study looks into the mimicking behaviour in both normal and asymmetric scenarios. It, then, considers the contagion between the USA and the BRICS stock markets. Finally, it examines herd behaviour in the wake of a major banking policy change concerning the bloc under study.

Design/methodology/approach

The current empirical analysis employs daily, weekly and monthly data points to estimate relevant herding parameters. Quantile regression specifications of Chang et al. (2000)'s dispersion method have been applied to detect herd activity. Also, dummy regression specifications have been used to examine the impact of various crises and strategically crucial events on the propensity to herd in the BRICS markets. The time period under consideration ranges from January 2011 until May 2019.

Findings

The relevant herding coefficients turn insignificant in most cases for normal and asymmetric scenarios except for China and South Africa. This can be traced to the anti-herding behaviour of investors, where individuals tend to diverge from the consensus. However, turbulence makes all stock markets to show some collective trading except Russia. Further, the Chinese stock market seems immune to the frictions in the US stock market. Finally, the Indian and South African markets witness significant herding during the formation of a common depository institution.

Practical implications

Most stock markets seem to herd during turbulence. This revelation is of strategic importance to the regulators and capital market managers. They have to be cautious during crises periods as the illusion of being secured with the masses ends up creating unprecedented frictions in the financial markets.

Originality/value

The present study seems to be the very first attempt to test the relevant distributions' tails for convergent behaviour in the BRICS markets.

Citation

Shrotryia, V.K. and Kalra, H. (2022), "Herding and BRICS markets: a study of distribution tails", Review of Behavioral Finance, Vol. 14 No. 1, pp. 91-114. https://doi.org/10.1108/RBF-04-2020-0086