Relationship Between Market Volatility and Trading Volume: Evidence from Amman Stock Exchange Izz Eddien N. Ananzeh (original) (raw)
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Market expectations of future return volatility play a crucial role in finance; we investigate the empirical relationship between return volatility and trading volume using data from the Amman Stock Exchange (ASE) for 27 individual stocks, using daily data for the period 2002-2012. The results indicate that trading volume significantly contributes to the return volatility process of stocks in Amman stock Exchange, as suggested in many studies. On the other hand, the results also signify that the Trading volume has no significant effect on the reduction of the volatility persistence for majority of stocks in the sample, challenging the existence of "Mixed Distribution Hypothesis" in Amman stock Exchange.
This paper investigates the volume-returnvolatility relationship for 25 individual stocks inthe Turkish stock market, using daily data for theperiod 1998-2005. The results indicate thattrading volume significantly contributes to thereturn volatility process of stocks in Turkish stockmarket, as suggested in many studies. On theother hand, the results also signify that thetrading volume has no significant effect on thereduction of the volatility persistence for majorityof stocks in the sample, challenging the presenceof “Mixed Distribution Hypothesis” in Turkishstock market. These results are consistent with theempirical findings of a number of studies inemerging markets, including with those done inTurkish stock market.
THE DYNAMIC RELATIONSHIP BETWEEN STOCK VOLATILITY AND TRADING VOLUME
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The objective of the study is to measure the relationship between trading volume and returns; and change in trading volume and returns of stocks in Pakistan.Various techniques such as Unit root tests and GARCH have been applied on the data to determine the relationship between aforesaid variables. For this purpose, weekly data of Karachi Stock Exchange (KSE-100 index) has been collected and analyzed from January 2000 to March 2012.The GARCH results indicate a significant positive relationship between trading volume and returns; and change in trading volume and returns.This relationship is of great importance to individuals from investment and policy making perspective as trading volume reflects information about market expectations, and its relationship with price can have important implications for trading, speculation, forecasting and hedging activities.
Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis
This paper investigates the empirical relationship between trading volume and conditional volatility using data from Amman Stock Exchange (ASE) within the framework of Mixed Distribution Hypothesis (MDH). Our sample covered 27 securities, which is most active stocks traded for the period span from 2002 to 2012. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) k Exchange model employed in order to test the persistence in the volatility of stock returns. Our results confirm positive and strong relationship between trading volume for individual stocks and conditional volatility of returns. Moreover, the degree of volatility persistence reduced through the process of adding the contemporaneous volume into the conditional variance equation of GARCH model, and this is according to the predictions of the Mixture of Distributions Hypothesis (MDH).
Trading Volume And Volatility In The Boursa Kuwait
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This paper presents the results of a study of the effect of daily trading volume on the persistence of timevarying conditional volatility for Kuwait Stock Exchange. The sample includes the market index, seven sectoral indices and 20 stocks. Whereas inclusion of contemporaneous volume in the equation of conditional variance does not reduce the persistence of volatility for the eight indices, this is not the case for individual companies. Furthermore, the lagged intraday volatility has higher predictive power for volatility than the lagged trading volume. These results lend further support to the mixture of distribution hypothesis at the level of firm, but not at the market and sectoral levels.
New Evidence on the Relation between Return Volatility and Trading Volume
In the empirical literature, it has been shown that there exists both linear and non-linear bi-directional causality between trading volumes and return volatility (measured by the square of daily return). We re-examine this claim by using realized volatility as an estimator of the unobserved volatility, adopting a stationary de-trended trading volume, and applying a more recent data sample with robustness tests over time. Our linear Granger causality test shows that there is no causal linear relation running from volume to volatility, but there exists an ambiguous causality for the reverse direction. In contrast, we find strong bi-directional non-linear Granger causality between these two variables. On the basis of the non-linear forecasting modeling technique, this study provides strong evidence to support the sequential information hypothesis and demonstrates that it is useful to use lagged values of trading volume to predict return volatility.
Investigation of Relation Between Stock Returns, Trading Volume, and Return Volatility
We use a bivariate GJR-GARCH model to investigate relationship between trading volume and stock returns. We apply our approach on Pakistan stock exchange on data from January 2012 to March 2016. Our major findings include that negative shock has a greater impact on volatility and investors are more prone to the negative news whereas according to GJR-GARCH good news has greater impact on stock return and there is a strong relationship exist between the trading volume, stock return and stock volatility.
Journal of Finance and Economics, 2018
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.
Journal of Applied Finance and Banking, 2012
It is a well-known fact that most of the asset returns tend to be skewed and heavytailed. Heavy tailed distributions such as the Student’s t distribution and Stable distribution are commonly used in finance to model asset returns that are heavy tailed. Additionally, Stable distribution allows not only for leptokurtosis but also skewness. Researchers that investigate the relationship between stock return volatility and trading volume have found a positive correlation between the volatility of returns and the volume traded. This paper focuses on this relationship by assuming the Student’s t and the Stable distributions for innovations. In this paper, GARCH and Threshold GARCH (TGARCH) models are applied on the Istanbul Stock Exchange National-100 Index with the purpose of analyzing the relationships between the volatility of stock returns and the trading volume.