Journal of Finance and Economics
ISSN (Print): 2328-7284 ISSN (Online): 2328-7276 Website: Editor-in-chief: Suman Banerjee
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Journal of Finance and Economics. 2018, 6(5), 193-200
DOI: 10.12691/jfe-6-5-5
Open AccessArticle

Modelling the Effects of Trading Volume on Stock Return Volatility Using Conditional Heteroskedastic Models

Edwin Moyo1, , Antony Gichuhi Waititu2 and Antony Ngunyi3

1Department of Statistics, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya

2Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

3Department of Statistics and Actuarial Science, Dedan Kimathi University of Science and Technology, Nyeri, Kenya

Pub. Date: September 09, 2018

Cite this paper:
Edwin Moyo, Antony Gichuhi Waititu and Antony Ngunyi. Modelling the Effects of Trading Volume on Stock Return Volatility Using Conditional Heteroskedastic Models. Journal of Finance and Economics. 2018; 6(5):193-200. doi: 10.12691/jfe-6-5-5


In this study, we analyzed the effects of trading volume as a proxy for the information arrival on stock return volatility and assess whether with the inclusion of trading volume in conditional variance equation, volatility persistence disappears using the generalized autoregressive conditional heteroscedasticity models; EGARCH and TGARCH. The analysis was done on the daily Nairobi Security Exchange (NSE) 20-share index and trading volume from 02/01/2009 to 02/06/2017 accounting for 2108 observations. The results of AR (2)-EGARCH (1,1) and AR (2)-TGARCH (1,1) models show that the relationship between trading volume and stock returns volatility is positive but not statistically significant implying that trading volume as a proxy of information flow can be considered generally as a poor source of volatility in stock returns. However, the results do not support the hypothesis that persistence in volatility disappears with the inclusion of trading volume in the conditional variance equation and this was consistent with the Student’s t-distribution and Generalized error term distribution assumption. We suggest that the AR (2)-EGARCH (1,1) model without trading volume with student t-distribution is a more suitable model to capture the main features of the stock returns such as the volatility clustering, the stock returns volatility and the leverage effect.

stock return volatility volume asymmetric GARCH models leverage effect

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[1]  K. S. Adesina. Modelling stock market return volatility: GARCH evidence from Nigerian stock exchange. International Journal of Financial Management, 3(3):37, 2013.
[2]  R. Vasudevan and S. Vetrivel. Forecasting stock market volatility using GARCH models: Evidence from the Indian stock market. Asian Journal of Research in Social Sciences and Humanities, 6(8):1565-1574, 2016.
[3]  W. Coffie. Modelling and forecasting the conditional heteroscedasticity of stock returns using asymmetric models: Empirical evidence from Ghana and Nigeria. Journal of Accounting and Finance, 15(5):109, 2015.
[4]  W. F. Sharpe. Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3): 425-442, 1964.
[5]  A. E. M. Ahmed and S. Z. Suliman. Modeling stock market volatility using GARCH models evidence from Sudan. International Journal of Business and Social Science, 2(23), 2011.
[6]  R. Kumar, H. Gupta, et al. Volatility in the Indian stock market: A case of individual securities. Journal of Academic Research in Economics, 1(1):43-54, 2009.
[7]  G. E. Tauchen and M. Pitts. The price variability-volume relationship on speculative markets. Econometrica: Journal of the Econometric Society, pages 485-505, 1983.
[8]  R. T. Mpofu. The relationship between trading volume and stock returns in the JSE securities exchange in South Africa. Corporate Ownership & Control, pages 2-10, 2012.
[9]  P. K. Clark. A subordinated stochastic process model with finite variance for speculative prices. Econometrica: journal of the Econometric Society, pages 135-155, 1973.
[10]  R. L. Crouch. The volume of transactions and price changes on the New York stock exchange. Financial Analysts Journal, 26(4): 104-109, 1970.
[11]  C. Wang. The effect of net positions by type of trader on volatility in foreign currency futures markets. Journal of Futures Markets, 22(5): 427-450, 2002.
[12]  P. N. Van. A good news or bad news has greater impact on the Vietnamese stock market? Munich Personal RePEc Archive, 61194, 2015.
[13]  R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2016. URL
[14]  A. Maqsood, S. Safdar, R. Shafi, and N. J. Lelit. Modeling stock market volatility using GARCH models: A case study of Nairobi Securities Exchange (NSE). Statistics, 7: 369-381, 2017.
[15]  C. G. Lamoureux and W. D. Lastrapes. Heteroskedasticity in stock return data: Volume versus garch effects. The journal of finance, 45(1): 221-229, 1990.
[16]  M. A. Thorlie, L. Song, X. Wang, and M. Amin. Modelling exchange rate volatility using asymmetric GARCH models (evidence from Sierra Leone). International Journal of Science and Research (IJSR), 3(11): 1206-1214, 2014.
[17]  T. Bollerslev. A conditionally heteroskedastic time series model for speculative prices and rates of return. The review of economics and statistics, pages 542-547, 1987.
[18]  T. Bollerslev and J. M. Wooldridge. Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric reviews, 11(2): 143-172, 1992.
[19]  R. S. Tsay and G. C. Tiao. Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary arma models. Journal of the American Statistical Association, 79(385): 84-96, 1990.
[20]  A. Wagala, D. K. Nassiuma, A. S. Islam, and J. W. Mwangi. Volatility modelling of the Nairobi Securities Exchange weekly returns using the arch-type models. International Journal of Applied, 2(3), 2012.
[21]  H. J. A. Ahmed, A. Hassan, and A. M. Nasir. 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 Innovations, 3(3): 146-158, 2005.
[22]  A. F. Darrat, S. Rahman, and M. Zhong. Intraday trading volume and return volatility of the djia stocks: A note. Journal of Banking & Finance, 27(10): 2035-2043, 2003.
[23]  K. O. Husborn, J. Nzyuko, and D. Omwansa. Relationship between trading volume and stock returns at the Nairobi Securities Exchange (NSE). International Journal of Management and Commerce Innovations, 5(2): 772-778, 2018.
[24]  E. Girard and R. Biswas. Trading volume and market volatility: Developed versus emerging stock markets. Financial Review, 42(3): 429-459, 2007.
[25]  G. Gursoy, A. Yuksel, and R. Biswas. Trading volume and stock market volatility: evidence from emerging stock markets, investment management and financial innovations. Financial Review, 5(4): 429-459, 2008.