Abstract
Volatility modelling is a key factor in equity markets for risk and portfolio management. This paper focuses on the use of a univariate generalized autoregressive conditional heteroscedasticity (GARCH) models for modelling volatility of the BRICS (Brazil, Russia, India, China and South Africa) stock markets. The study was conducted under the assumptions of seven error distributions that include the normal, skewed-normal, Student’s t, skewed-Student’s t, generalized error distribution (GED), skewed-GED and the generalized hyperbolic (GHYP) distribution. It was observed that using an ARMA(1, 1)-GARCH(1, 1) model, volatilities of the Brazilian Bovespa and the Russian IMOEX markets can both be well characterized (or described) by a heavy-tailed Student’s t distribution, while the Indian NIFTY market’s volatility is best characterized by the generalized hyperbolic (GHYP) distribution. Also, the Chinese SHCOMP and South African JALSH markets’ volatilities are best described by the skew-GED and skew-Student’s t distribution, respectively. The study further observed that the persistence of volatility in the BRICS markets does not follow the same hierarchical pattern under the error distributions, except under the skew-Student’s t and GHYP distributions where the pattern is the same. Under these two assumptions, i.e. the skew-Student’s t and GHYP, in a descending hierarchical order of magnitudes, volatility with persistence is highest in the Chinese market, followed by the South African market, then the Russian, Indian and Brazilian markets, respectively. However, under each of the five non-Gaussian error distributions, the Chinese market is the most volatile, while the least volatile is the Brazilian market.
Highlights
The British economist Jim O’Neill conceptualized the word BRICS as an acronym of five key emerging regional economies of Brazil, Russia, India, China and South Africa
This paper focuses on modelling volatility of the BRICS stock returns using generalized autoregressive conditional heteroscedasticity (GARCH) models under the assumption of the stated seven error distributions
The pattern of volatility persistence in each of the BRICS markets can be determined by a non-Gaussian distribution like the generalized error distribution (GED), Student’s t and generalized hyperbolic (GHYP)
Summary
The British economist Jim O’Neill conceptualized the word BRICS as an acronym of five key emerging regional economies of Brazil, Russia, India, China and South Africa. The study will examine the dynamics of market returns volatility in the stock markets of the five BRICS nations These markets are selected because they, as a team, are progressively and increasingly becoming a propelling force on global economic growth [38] and they are of particular interest to investors and practitioners. The findings showed, at 1% significance level, a unidirectional spill-overs from the bond to the stock markets in the UK, Germany and U.S For South Africa, France and Brazil, the findings observed a bidirectional volatility spill-overs between the bond and equity markets, but no definite directional conclusion was made for Canada, Italy, Japan, China and India. Salisu and Gupta [34] analysed the response effect of the BRICS stock markets volatility on their oil shocks using the Generalised Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling (GARCH-MIDAS) model.
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