This paper aims at investigating the effect of stock returns on trading volume and the trading volume on volatility. To accomplish the study objective, daily data from 1 April 2014 to 30 June 2020 are used. The GLS model is employed to investigate the contemporaneous relationship between the stock returns and trading volume. The EGARCH (1, 1) model is used to explore the volume–volatility relationships, and the causal relationship of stock returns–trading volumes–volatility is attained through the Granger causality test. The study finds that the effect of stock returns on trading volume is asymmetric. It indicates that the negative price changes have a less considerable impact on trading volume than the non-negative price changes. The study also supports the mixture of the distribution hypothesis that postulated the volume–volatility relationship to be positive. The volatility persistence level remains high, even after the conditional volatility model incorporates trading volumes as an exogenous variable. Further, the analysis also reveals that stock return causes trading volume and not vice versa; however, trading volumes cause return volatility.
AhmedH. J. A., HassanA., & NasirA. M. D. (2005). The relationship between trading volume, volatility and stock market returns: A test of mixed distribution hypothesis for a pre and post crisis on Kuala Lumpur stock exchange. Investment Management and Financial Innovation, 2(3), 146–158.
2.
AlsubaieA., & NajandM. (2009). Trading volume time-varying conditional volatility and asymmetric volatility spill over in the Saudi stock market. Journal of Multinational Financial Management, 19(2), 169–181.
3.
AndersenT. G. (1996). Return volatility and trading volume: An information flow interpretation of stochastic volatility. Journal of Finance, 51(1), 169–204.
4.
BohlM. T., & HenkeH. (2003). Trading volume and stock market volatility: The polish case. International Review of Financial Analysis, 12(5), 513–525.
5.
BrailsfordT. J. (1996). The empirical relationship between trading volume, returns, and volatility. Accounting and Finance, 36(1), 89–111.
6.
CanarellaG., & PollardS. K. (2014). The mixture of distribution hypothesis and the Russian stock market. Journal of Business & Economics Research, 1(11), 43–58.
7.
ChenG. M., FirthM., & RuiO. (2001). The dynamic relation between stock returns, trading volume and volatility. The Financial Review, 36(3), 153–174.
8.
ChoiK.-H., JiangZ.-H., KangS. H., & YoonS.-M. (2012). Relationship between trading volume and asymmetric volatility in the Korean stock market, Modern Economy, 3(5), 584–589.
9.
ClarkP. (1973). A subordinated stochastic process model with finite variances for speculative prices. Econometrica, 41(1), 135–155.
10.
DarratA. F., RahmanS., & ZhongM. (2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking and Finance, 27(10), 2035–2043.
11.
EppsT. W. (1975). Security price changes and transaction volume: Theory and evidence. American Economic Review, 65(4), 586–597.
12.
EppsT. W., & EppsM. L. (1976). The stochastic dependence of security price change and transaction volumes: Implication for the mixture of distribution hypothesis. Econometrica, 44(2), 305–325.
13.
GallantA. R., RossiP. E., & TouchenG. E. (1992). Stock prices and volume. Review of Financial Studies, 5(2), 199–242.
14.
HarrisL. (1987). Transaction data tests of mixture of distribution hypothesis. Journal of Financial and Quantitative Analysis, 22(2), 29–45.
15.
HuangB., & YangC. (2001). An empirical investigation of trading volume and return volatility of the Taiwan stock market. Global Finance Journal, 12(1), 55–77.
16.
JenningsR. H., StarksL. T., & FellinghamJ. C. (1981). An equilibrium model of asset trading with sequential information arrival. Journal of Finance, 36(1), 143–161.
17.
JonesC. M., KaulG., & LipsonM. L. (1994). Transaction, volume and volatility. Review of Financial Studies, 7(4), 631–651.
18.
KarpoffJ. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22(1), 109–126.
19.
LamoureuxC. G., & LastrapesW. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. Journal of Finance, 45(1), 221–229.
20.
LeeB. S., & RuiO. M. (2002). The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence. Journal of Banking and Finance, 26(1), 51–78.
21.
MahajanS., & SinghB. (2009). The empirical investigation of relationship between return, volume and volatility dynamics in Indian stock markets. Eurasian Journal of Business and Economics, 2(4), 113–137.
22.
NaikP. K., & PadhiP. (2015). Stock market volatility and equity trading volume: Empirical examination from Brazil, Russia, India and China (BRIC). Global Business Review, 16(5), 28–45.
23.
NaikP. K., PadhiP., GuptaR. (2018). The relationship between stock market volatility and trading volume: Evidence from South Africa. The Journal of Developing Areas, 52(1), 99–114.
24.
NelsonD. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
25.
PyunC. S., LeeS. Y., & NamK. (2000). Volatility and information flow in emerging equity market: A case of the Korean stock exchange. International Review of Financial Analysis, 9(4), 405–420.
26.
SabbaghiO. (2011). Asymmetric volatility and trading volume: The G5 evidence. Global Finance Journal, 22(2), 169–181.
TripathyN. (2010). The empirical relationship between trading volumes and stock return volatility in Indian stock market. European Journal of Economics, Finance and Administrative Sciences, 24, 59–77.
29.
WangP., WangP., & LiuA. (2005). Stock return volatility and trading volume: Evidence from the Chinese stock market. Journal of Chinese Economic and Business Studies, 3(1), 39–54.
