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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.

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Article

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

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


Journal of Finance and Economics. 2018, Vol. 6 No. 5, 193-200
DOI: 10.12691/jfe-6-5-5
Copyright © 2018 Science and Education Publishing

Cite this paper:
Edwin Moyo, Antony Gichuhi Waititu, 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.

Correspondence to: Edwin  Moyo, Department of Statistics, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya. Email: emoyo@aims.ac.tz

Abstract

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.

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