Abstract
Executive Summary Cross-sectional volatility measures dispersion of security returns at a particular point of time. It has received very little focus in research. This article studies the cross-section of volatility in the context of economies of Brazil, Russia, India, Indonesia, China, South Korea, and South Africa (BRIICKS). The analysis is done in two parts. The first part deals with systematic volatility (SV), that is, cross-sectional variation of stock returns owing to their exposure to market volatility measure ( French, Schwert, & Stambaugh, 1987 ). The second part deals with unsystematic volatility (UV), measured by the residual variance of stocks in a given period by using error terms obtained from Fama–French model. The study finds that high SV portfolios exhibit low returns in case of Brazil, South Korea, and Russia. The risk premium is found to be statistically significantly negative for these countries. This finding is consistent with Ang et al. and is indicative of hedging motive of investors in these markets. Results for other sample countries are somewhat puzzling. No significant risk premiums are reported for India and China. Significantly positive risk premiums are observed for South Africa and Indonesia. Further, capital asset pricing model (CAPM) seems to be a poor descriptor of returns on systematic risk loading sorted portfolios while FF is able to explain returns on all portfolios except high SV loading portfolio (i.e., P1) in case of South Africa which seems to be an asset pricing anomaly. It is further observed that high UV portfolios exhibit high returns in all the sample countries except China. In the Chinese market, the estimated risk premium is statistically significantly negative. This negative risk premium is inconsistent with the theory that predicts that investors demand risk compensation for imperfect diversification. The remaining sample countries show significantly positive risk premium. CAPM does not seem to be a suitable descriptor for returns on UV sorted portfolios. The FF model does a better job but still fails to explain the returns on high UV sorted portfolio in case of Brazil and China and low UV sorted portfolio in South Africa. The findings are relevant for global fund managers who plan to develop emerging market strategies for asset allocation. The study contributes to portfolio management as well as market efficiency literature for emerging economies.
Highlights
This article studies the crosssection of volatility in the context of economies of Brazil, Russia, India, Indonesia, China, South Korea, and South Africa (BRIICKS)
The first part deals with systematic volatility (SV), that is, cross-sectional variation of stock returns owing to their exposure to market volatility measure (French, Schwert, & Stambaugh, 1987)
The second part deals with unsystematic volatility (UV), measured by the residual variance of stocks in a given period by using error terms obtained from Fama–French model
Summary
Cross-sectional volatility measures dispersion of security returns at a particular point of time. For our study, we use the emerging market acronym BRIICKS—Brazil, Russia, India, Indonesia, China, South Korea, and South Africa representing the world’s major emerging economies These markets are gaining importance because global investors tend to combine them with their developed market investments for risk diversification purpose. There is a virtual absence of empirical work on cross-sectional volatility anomaly.1 It is important for policy makers, global investment managers as well as the academia to understand if there are any significant differences in the cross-sectional volatility patterns in stock returns for mature and emerging markets and whether these observable patterns can be explained by common risk factors. Do the asset pricing models like the CAPM and the Fama–French Model explain the returns on the portfolios formed on the basis of volatility?
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