THE DYNAMIC RELATIONSHIP BETWEEN STOCK VOLATILITY AND TRADING VOLUME (original) (raw)
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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 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.
Time Series Analysis of Stock Market Volatility in Pakistan
Asian Journal of Probability and Statistics, 2020
The stock market in an emerging country like Pakistan has been volatile from the earliest times. This paper studies the volatility of Pakistan Stock Exchange (PSX) (using Karachi Stock Exchange 100 Index (KSE-100) as a proxy) through the application of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family models. The sample period consists of 4831 daily observations for the 19 year trading period (from 2000 to 2019). Symmetric GARCH (2, 1), asymmetric EGARCH (1, 1), GJR-GARCH (1, 1) and APARCH (1, 1) models were used under Gaussian distributional assumptions. The results validate the empirical findings of previous studies conducted in Pakistan that log returns of KSE-100 Index are characterized by volatility clustering, time-variability, leptokurtic distribution with dominant ARCH and GARCH effects. An interesting feature of Pakistan Stock Exchange revealed by asymmetric models (used in the study) is that PSX is more volatile to good news than bad news. Moreover E...
This study investigates stock market volatility asymmetry and its relationship with equity trading volume in the Indian stock market using daily data over the period from 2nd January 1997 to 30th May 2013. We employ GARCH, EGARCH and GJR-GARCH models to examine the volatility pattern in the stock market. We also decompose the conditional variance into a transitory and permanent component, modeled by asymmetric CGARCH, in order to check the short run and long run movements of volatility. Further, contemporaneous trading volumes are augmented in the volatility model to empirically verify the validity of Mixture of Distribution Hypothesis (MDH) and the level of volatility persistence. The findings show significant volatility asymmetry in the Indian equity market, supporting the leverage effect hypothesis. Secondly, we find a positive contemporaneous relationship between volume and volatility, validating the argument of MDH. Moreover, the results show that the volatility shocks are highly persistent even after incorporating trading volume, contradicting the seminal findings of Lamoureux and Lastrapes (1990).
Measuring Volatility Using Garch Models : An Application to Selected Stock of Dhaka Stock Exchange
International Journal of Advanced Research, 2020
Stock market is an important part of economy of a country. Measuring stock market volatility is an vital issue in finance. There are various models to evaluate volatility. The daily return series shows that there is a variation of closing prices of AB bank. The data of AB bank includes daily closing prices from 01-01-2015 to 05-10-2017 from Dhaka Stock Exchange (DSE) library to forecast phenomena of stock market volatility. We use GARCH models to assess the volatility of stocks from banking sector and find that GARCH (1,1) model is best for measuring volatility of the stocks of AB bank. Once if we measure the volatility then it is possible to make best prediction when to buy and when to sell a stock.
This paper examines the empirical relationship between return, volume and volatility dynamics of stock market by using daily data of the Sensitive Index (SENSEX) during the period from October 1996 to March 2006. The empirical analysis provides evidence of positive and significant correlation between volume and return volatility that is indicative of the both mixture of distribution and sequential arrival hypothesis of information flow. Causality from volatility to volume can be seen as some evidence that new information arrival might follow a sequential rather than a simultaneous process. In addition, GARCH (1,1) documents the small declines in persistence of variance over time if one includes trading volume as a proxy for information arrivals in the equation of conditional volatility and ARCH and GARCH effects remain significant, which highlights the inefficiency in the market. This finding supports the proposition that volume provides information on the precision and dispersion of information signals, rather than serving as a proxy for the information signal itself.
Modeling volatility on the Karachi Stock Exchange, Pakistan
Purpose The current paper aims to fill the gap in the literature by analyzing the nature of volatility of the Karachi Stock Exchange (KSE) -100 index and also develops an understanding as to which model proves to be the most suitable model for measuring volatility among the models used. The study contributes significantly to the literature as compared to the limited previous work done in Pakistan as it covers a longer time period from the introduction of the KSE-100 index on November 2, 1991 to December 31, 2013 for three types of data i.e., daily, weekly and monthly. In addition, to analyze the impact of global financial crises upon volatility, the data is divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods. Design/Methodology/Approach This study employs an advanced set of volatility models such as Autoregressive Conditional Heteroscedasticity [ARCH (1)]; Generalized Autoregressive Conditional Heteroscedasticity [GARCH (1, 1)]; GARCH in Mean [GARCH-M (1, 1)]; Exponential GARCH [E-GARCH (1, 1)]; Threshold GARCH [T-GARCH (1, 1)]; Power GARCH [P-GARCH (1, 1)] and also a simple Exponentially Weighted Moving Average (EWMA) model. Findings The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity, and volatility clustering. However, the heteroscedasticity is absent only in the monthly returns making only EWMA model to be used in monthly series to measure the volatility level. P-GARCH (1, 1) model proves to be a better model for modeling the volatility in case of daily returns; while regarding the weekly data GARCH (1, 1) model proves to be the most appropriate based on SIC and LL criterion. The study shows a high persistence of volatility, a mean reverting process, and absence of risk premium in the KSE market with an insignificant leverage effect only in case of weekly returns; however a significant leverage effect is reported regarding the daily series of the KSE-100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that sub-periods demonstrated a slightly low volatility and global economic crisis did not cause a rise in volatility level. Originality/Value The literature about the volatility modeling in the Pakistan market depicts a limited literature focus on few models for relatively small sample size. The current thesis attempts to overcome these limitations and employs diverse models for three types of data series (daily, weekly and monthly). In addition, Pakistani economy shows turmoil throughout its history, which ranges from mild shocks to extreme shocks. This paper measures the impact of those shocks upon the volatility level of the KSE. Applications The research provides some insights for policy makers as well as investors who are concerned about the fluctuations of the KSE-100 index in Pakistan. For example, the significant ARCH effect may imply that the institutional investors who do not trade very frequently also hold dominance with respect to the movement of the price of stocks. As when they trade, it is in the large bulk and this heavy trade holds a significant impact on the price movements of the stock (Husain and Uppal, 1999). The skewness with the negative value in all return series imply that there are more chances of earning negative returns than the positive returns which refers towards the conservative attitude of investors towards investment in the KSE market (Mittal and Goyal, 2012; Kaluo and Friday, 2012).. The findings also reveal a high persistence of volatility and it contributes to the existence of the impact of shock, observed in present, for a longer time on future returns. The high volatility presence makes it possible to earn high profit but it also leads towards an inefficient market (Mittal and Goyal, 2012). A presence of an insignificant risk premium implies that investors are unable to earn returns above the average by taking the higher risk. The leverage parameter finds significance in case of daily returns which implies that negative shocks account for the greater volatility in the KSE market; however, the weekly data do not reveal significant asymmetrical or leverage effect. Key Words: ARCH, GARCH, EWMA, Global Financial Crisis, Pakistan, Volatility
On stock returns volatility and trading volume of the nairobi securities exchange index
RMS: Research in Mathematics & Statistics, 2021
This study attempts to put forward a framework that can be utilized to model the dynamics of the underlying returns on asset. The intention is to probe the dynamic connection between volatility of stock returns and trading volume of the Nairobi Securities Exchange (NSE20) index. The consequence of incorporating trading volume in the equation for conditional variance of the generalized autoregressive conditional heteroscedasticity (GARCH) model on volatility persistence is investigated. Further, this study brings into play GARCH, GARCH-M, and EGARCH models conditioned to normal, student-t and generalized error distributions to model the dynamic structure of the NSE20 index for the period 2 January 2001 to 31 December 2017. The results disclose some well-known stylized facts of returns on stock, for instance, volatility clustering, heavy tails, leverage effects, and leptokurtic distribution. The estimates of parameters of the three models, that is, GARCH (1, 1), GARCH-M (1, 1), and EG...
2013
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.