scholarly journals Information arrival and volatility: Evidence from the Saudi Stock Exchange (Tadawul)

2017 ◽  
Vol 64 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Hassan Ezzat ◽  
Berna Kirkulak-Uludag
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Equity Market ◽  
Conditional Variance ◽  
Market Volatility ◽  
Bad News ◽  
Leverage Effect ◽  
Asymmetric Effect ◽  
Mixture Of Distributions ◽  
Variance Equation

This paper investigates the validation of the Mixture of Distributions Hypothesis (MDH) using trading volume and number of trades as contemporaneous proxies for information arrival in 15 sector indices of the Saudi Stock Exchange (Tadawul) using the TGARCH model. Findings provide strong evidence for the validity of the MDH for the Saudi market. Volatility persistence decreases when the trading volume and the number of trades are included in the conditional variance equation. The most striking finding is that contemporaneous number of trades is a better proxy for information arrival than trading volume, interacting with volatility in a manner anticipated under the MDH. This can be attributed to the unique characteristic of the Saudi equity market where only domestic investors are allowed to execute trade transactions. Further, the results reveal that the leverage effect was amplified, indicating a more pronounced asymmetric effect of bad news on volatility.

Day Of The Week And Its Effect On Stock Market Volatility: Evidence From An Emerging Market

2013 ◽  
Vol 29 (6) ◽  
pp. 1727 ◽  
Author(s):  
Omar Farooq ◽  
Mohammed Bouaddi ◽  
Neveen Ahmed
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Emerging Market ◽  
International Markets ◽  
Conditional Variance ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Foreign Investors ◽  
High Volatility ◽  
Day Of The Week

This paper investigates the day of the week effect in the volatility of the Saudi Stock Exchange during the period between January 7, 2007 and April 1, 2013. Using a conditional variance framework, we find that the day of the week effect is present in the volatility. Our results show that the lowest volatility occurs on Saturdays and Sundays. We argue that due to the closure of international markets on Saturdays and Sundays, there is not enough activity in the Saudi Stock Exchange. As a result, the volatility is the lowest on these days. Our results also show that the highest volatility occurs on Wednesdays. We argue Wednesday, being the last trading day of the week, corresponds with the start of four non-trading days (Thursday through Sunday) for foreign investors. Fearing that they will be stuck up with stocks in case some unfavorable information enters the market, foreign investors tend to exit the market on Wednesdays. As a result of excessive trading, there is high volatility on Wednesdays.

Volatility persistence and trading volume in an emerging futures market

2010 ◽  
Vol 11 (3) ◽  
pp. 296-309 ◽  
Author(s):  
Pratap Chandra Pati ◽  
Prabina Rajib
Keyword(s):  
Capital Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Market Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Conditional Volatility ◽  
Content Type ◽  
Futures Contract

PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.

scholarly journals Expiration-Day Effects of Index Futures in a Frontier Market: The Case of Ho Chi Minh Stock Exchange

2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Anh Thi Kim Nguyen ◽  
Loc Dong Truong ◽  
H. Swint Friday
Keyword(s):  
Regression Models ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Volatility ◽  
Market Returns ◽  
Index Futures ◽  
Daily Return ◽  
Market Return ◽  
Index Stock ◽  
Level Of Significance

This study employs OLS, GARCH and EGARCH regression models to test the expiration-day effects of index stock futures on market returns, volatility and trading volume for the Ho Chi Minh Stock Exchange (HOSE). Data used in this study is from a daily return series of the VN30-Index for the period from 10August 2017 through 30 June 2020. The results derived from GARCH(1,1) and EGARCH(1,1) models consistently confirm that Index futures expiration-day effects on market returns exists in the HOSE. Specifically, the average market return for expiration days is significantly lower than other trading days, by 0.13% at the 5% level of significance. However, the results obtained from the regression models indicate that the expiration-day has no impact on market volatility and trading volume.

Intraday Variability and Trading Volume: Evidence from National Stock Exchange

2020 ◽  
Vol 19 (3) ◽  
pp. 271-295
Author(s):  
Aravind Sampath ◽  
Arun Kumar Gopalaswamy
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Equity Market ◽  
Indian Market ◽  
Jel Codes ◽  
Tick Data ◽  
High Volatility ◽  
Microstructure Effects ◽  
Opening And Closing ◽  
The Impact

In this article, we investigate patterns in returns, volume and volatility and analyse the volume–return relationship using tick-by-tick data from the Indian equity market. Based on descriptive measures and regression frameworks, we document three important findings. First, we report unusually high volatility, trading volume and number of trades during the opening and closing minutes of the market depicting a ‘U’-shaped curve, implying high market activity during these periods. Second, while accounting for trading volume, we observe that volatility is not significantly different between mid-day period and evening period as compared to the normal ‘U’ curve. Finally, we document a significant positive relationship between intraday volume and price movements controlling for microstructure effects. The impact of positive returns on trading volume is higher than the impact of negative returns, implying the presence of return–volume asymmetry in the Indian market. JEL Codes: G12, G15

scholarly journals PERAMALAN VOLATILITAS RETURN SAHAM MENGGUNAKAN METODE ASYMMETRIC POWER ARCH (APARCH)

2020 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
JUITA HARYATI SIDADADOLOG ◽  
I WAYAN SUMARJAYA ◽  
NI KETUT TARI TASTRAWATI
Keyword(s):  
Stock Return ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Leverage Effect ◽  
Asymmetric Effect ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
Effect Condition

Model APARCH is one of the asymmetric GARCH models. These models are able to capture the incidence of good news and bad news in the volatility. The APARCH model has an asymmetric coefficient to cope with leverage effect by modeling a leverage that has heteroscedasticity and asymmetric effect condition. The results of this research were obtained by the appropriate APARCH model. The model is the APARCH(1,2) model because all parameters are significant. Thus, proceeds from the volatility of stock return for the next 14 days with the model volatility APARCH(1,2) increased from period one to period fourteen.

scholarly journals Have Stock Markets Become Less Volatile After the Great Recession?

2019 ◽  
Vol 10 (3) ◽  
pp. 10
Author(s):  
Talla M Aldeehani
Keyword(s):  
Financial Markets ◽  
Great Recession ◽  
Trading Volume ◽  
Global Financial Crisis ◽  
International Markets ◽  
Market Volatility ◽  
Asymmetric Effect ◽  
The Great Recession ◽  
Valid Predictor ◽  
Before And After

This paper investigates volatility modeling in light of the 2008 global financial crisis. The study was motivated by the measures and regulations introduced by most of the countries following the shock to stabilize their financial markets. The theoretical proposition is that these measures should succeed in reducing volatility which would be modeled differently following the crisis. The adopted ARMA-GARCH process included positive and negative trading volume change to capture the asymmetric effect of trading volume on market volatility for seven international markets. The results indicate that the majority of these markets were not so successful in reducing volatility following the crisis. There is evidence of volatility persistence which dissipates very quickly. Although volatility is modeled differently before and after the crisis, each market is modeled uniquely. The effect of trading volume was found to be asymmetric. Only positive change was a valid predictor. Detailed discussions of the results, implications, and recommendations are provided.

scholarly journals The Impact Of Political Instability Driven By The Tunisian Revolution On Stock Market Volatility: Evidence From Sectorial Indices

2018 ◽  
Vol 34 (2) ◽  
pp. 339-354 ◽  
Author(s):  
Salma Zaiane
Keyword(s):  
Stock Exchange ◽  
Bad News ◽  
Good News ◽  
Stock Market Volatility ◽  
Asymmetric Effect ◽  
Political News ◽  
Stock Indices ◽  
Terrorist Acts ◽  
Tunisian Revolution ◽  
The Impact

The aim of this paper is to study the impact of political uncertainty, driven by the Tunisian Revolution, on return and volatility of major sectorial stock indices in the Tunisian Stock Exchange. We specifically use EGARCH (1.1) model from 01/12/2010 to 31/08/2016. This model is applied to the daily returns relevant to ten sectorial stock indices and to the Tunisian benchmark index (TUNINDEX). To test the impact of political news on returns and volatility, we divided them into two groups (good and bad news). Our results show that both of good and bad news have increased the volatility of major selected indices, including the TUNINDEX. However, the return of all indices are not affected by the political news. We then examined the impact of terrorism on the behavior of indices return and volatility. Results show that the Tunisian market responds significantly to terrorist acts. Hence, the return declines and the volatility increase the day of terrorist attacks. Furthermore, results confirm that bad news have stronger effect on the volatility than good news, which reveal the asymmetric effect of volatility.

Self-attribution, Overconfidence and Dynamic Market Volatility in Indian Stock Market

2018 ◽  
Vol 21 (4) ◽  
pp. 970-989
Author(s):  
Venkata Narasimha Chary Mushinada ◽  
Venkata Subrahmanya Sarma Veluri
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Empirical Evaluation ◽  
Market Volatility ◽  
Crisis Period ◽  
Asymmetric Volatility ◽  
Attribution Bias ◽  
Indian Stock Market ◽  
Overconfidence Bias

The article provides an empirical evaluation of self-attribution, overconfidence bias and dynamic market volatility at Bombay Stock Exchange (BSE) across various market capitalizations. First, the investors’ reaction to market gain when they make right and wrong forecasts is studied to understand whether self-attribution bias causes investors’ overconfidence. It is found that when investors make right forecasts of future returns, they become overconfident and trade more in subsequent time periods. Next, the relation between excessive trading volume of overconfident investors and excessive prices volatility is studied. The trading volume is decomposed into a first variable related to overconfidence and a second variable unrelated to investors’ overconfidence. During pre-crisis period, the analysis of small stocks shows that conditional volatility is positively related to trading volume caused by overconfidence. During post-crisis period, the analysis shows that the under-confident investors became very pessimistic in small stocks and tend to overweight the future volatility. Whereas, the analysis of large stocks indicates that the overconfidence component of trading volume is positively correlated with the market volatility. Collectively, the empirical results provide strong statistical support to the presence of self-attribution and overconfidence bias explaining a large part of excessive and asymmetric volatility in Indian stock market.

scholarly journals ANALISIS RETURN DAN LIKUIDITAS SAHAM SEBELUM DAN SESUDAH PENGUMUMAN RIGHT ISSUE DI BURSA EFEK INDONESIA

2018 ◽  
Author(s):  
Irdha Yusra
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Secondary Data ◽  
Bad News ◽  
Good News ◽  
Volume Activity ◽  
Significant Difference ◽  
Before And After ◽  
Rights Issue

The purpose of this study was to analyze the abnormal returns and trading volume activity before and after the announcement of the rights issue. This research is the event study using secondary data. 33 companies listed in Indonesia Stock Exchange from 2005 to 2009 were sampled using a purposive sampling method, which consists of 9 samples (good news) and 24 samples (bad news). The results of this study showed that there was no significant difference in abnormal return observation period 5 days, 15 days, 60 days, 90 days, 180 days before and after the announcement of the rights issue in the group of good news and bad news. While the volume of trading activity, trading volume activity differences are significant at the 5 day period prior to the announcement of the rights issue after the group bad news.

scholarly journals Study of the Impact of Different Investor Behaviours on Market Volatility

2020 ◽  
pp. 46-55
Author(s):  
Abdolkarim Moghadam ◽  
Hamidreza Ghiabi
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Correlation Study ◽  
Market Volatility ◽  
Sample Size Determination ◽  
Considerable Impact ◽  
Investor Behaviour ◽  
Variance Homogeneity ◽  
Investor Trading ◽  
The Impact

The purpose of this research is to study the impact of different investor behavior on the volatility of the bourse market. The field study consists of the companies listed on the stock exchange during the years 2012-2016. In this study, the different investor behaviour is considered the independent variable and the market volatility is the dependent variable. The present research is an applied study, in case the classification of researches in characteristics and methodology is considered, this study is considered descriptive research based on its characteristics and it is in the correlation study category based on its methodology. In this study collecting data and information has been done by library method and the data compilation has been fulfilled by referring to the financial statements, explanatory notes and monthly magazine of the bourse. In sample size determination based on data collecting system, 114 companies have been selected as the sample statistics. In order to describe and summarize the collected data descriptive and inferential statistics have been utilized. In analyzing the data first pre-test of variance homogeneity, Limer F test, Hausman test and JB test and then the multivariable regression (Eviews software) for confirmation or rejection of the hypothesis of the research has been used. The results show that the different investor behaviour consisting of the trading value of capital, investors net business flow and the investor trading volume share has a considerable impact on the volatility of the stock exchange.

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