Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets
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,...
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doaj-f525f11b83d4476a909d0d899bac77d82021-04-02T13:03:45ZengSAGE PublishingVikalpa0256-09092016-09-014110.1177/0256090916650951Cross-sectional Volatility and Stock Returns: Evidence for Emerging MarketsSanjay Sehgal0Vidisha Garg1Professor of Finance at the Department of Financial Studies, University of Delhi, India. He is PhD in Finance from Delhi School of Economics and post-doctoral Commonwealth Research Fellow from the London School of Economics, UK. He has completed 7 major research projects and written 136 research papers in refereed journals. His areas of teaching and research are investment valuation, portfolio management, financial econometrics, and equity research. e-mail: Assistant Professor at the Department of Commerce, Maitreyi College, University of Delhi, India. She is PhD in Finance from the Department of Financial Studies, University of Delhi. She has teaching experience of around six years. She has published four research papers and a few research articles. Her areas of teaching and research are investment and portfolio management. e-mail: 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.https://doi.org/10.1177/0256090916650951 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanjay Sehgal Vidisha Garg |
spellingShingle |
Sanjay Sehgal Vidisha Garg Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets Vikalpa |
author_facet |
Sanjay Sehgal Vidisha Garg |
author_sort |
Sanjay Sehgal |
title |
Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets |
title_short |
Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets |
title_full |
Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets |
title_fullStr |
Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets |
title_full_unstemmed |
Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets |
title_sort |
cross-sectional volatility and stock returns: evidence for emerging markets |
publisher |
SAGE Publishing |
series |
Vikalpa |
issn |
0256-0909 |
publishDate |
2016-09-01 |
description |
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. |
url |
https://doi.org/10.1177/0256090916650951 |
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