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 Studentâs 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.
Highlights
The study of volatility in financial markets is of great importance to investors in the managing of risk as it provides a degree of uncertainty on their investment
We observe that the stock returns series have a negative daily mean suggesting that they decrease slightly over time while the average mean daily trading volume is positive implying that the trading volume increase slightly with time
We modeled the effects of trading volume on stock return volatility using the AR (2)-Exponential GARCH (EGARCH) (1,1) and AR (2)-Threshold GARCH (TGARCH) (1,1) models under the student’s tdistribution and Generalized Error Distribution (GED)
Summary
The study of volatility in financial markets is of great importance to investors in the managing of risk as it provides a degree of uncertainty on their investment. Reference [1] articulates that financial analysts and investors in financial markets are concerned with the unpredictability on asset return investment that are attributed to business performance instability and varying market prices. Are better modeled by nonlinear processes which include; ARCH, GARCH, TGARCH, EGARCH, PGARCH and many others. Financial time series returns often display volatility clustering. Reference [2] outlines the most essential financial time series features as; they tend to have leptokurtic distribution, leverage effect, skewness and volatility clustering. The standard ARCH/GARCH model can model the leptokurtosis, skewness and volatility clustering. [3] shows that the standard model is unable to capture the dynamics of an important feature of financial time series known as the leverage effect i.e. cannot model this asymmetric behaviour of stock returns The standard ARCH/GARCH model can model the leptokurtosis, skewness and volatility clustering. [3] shows that the standard model is unable to capture the dynamics of an important feature of financial time series known as the leverage effect i.e. cannot model this asymmetric behaviour of stock returns
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