scholarly journals Contemporaneous Relationship between Trading Volume and Stock Returns Volatility : Evidence from Nepalese Stock Market

2017 ◽  
Vol 10 (1) ◽  
pp. 40-63
Author(s):  
Shivaram Shrestha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Stock Return ◽  
Efficient Market Hypothesis ◽  
Least Square ◽  
Return Volatility ◽  
Monthly Data ◽  
Stock Return Volatility ◽  
Mixture Of Distribution Hypothesis

This paper examines the contemporaneous relation between trading volume and stock returns volatility for Nepalese stock market using monthly data for the period 2005 mid-July to 2017 mid-April. The study uses ordinary least square method and analyzes whether rising price leads to higher volume or vice versa. The study also investigates the association between trading volume and stock returns volatility based on monthly data of NEPSE index and examines the effects of trading volume on stock returns volatility using GARCH (1, 1) model. The study finds positive contemporaneous relationship between trading volume and stock return volatility. The study result indicates that the relationship between trading volume and return volatility is asymmetric. The findings strongly support the hypothesis that higher trading volume is associated with an increase in stock return volatility, but offers little support to the sequential arrival hypothesis and the mixture of distribution hypothesis. Finally, the findings support the weak-form efficient market hypothesis in Nepalese stock market.

scholarly journals THE VOLATILITY TRANSMISSION OF MAIN GLOBAL STOCK'S RETURN TO INDONESIA

2017 ◽  
Vol 20 (2) ◽  
pp. 229-256
Author(s):  
Linda Karlina Sari ◽  
Noer Azam Achsani ◽  
Bagus Sartono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Stock Return ◽  
Global Financial Crisis ◽  
Symmetric Model ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Volatility Transmission ◽  
The Impact

Stock return volatility is a very interesting phenomenon because of its impact on global financial markets. For instance, an adverse shocks in one country’s market can be transmitted to other countries’ market through a particular mechanism of transmission, causing the related markets to experience financial instability as well (Liu et al., 1998). This paper aims to determine the best model to describe the volatility of stock returns, to identify asymmetric effect of such volatility, as well as to explore the transmission of stocks return volatilities in seven countries to Indonesia’s stock market over the period 1990-2016, on a daily basis. Modeling of stock return volatility uses symmetric and asymmetric GARCH, while analysis of stock return volatility transmission utilizes Vector Autoregressive system. This study found that the asymmetric model of GARCH, resulted from fitting the right model for all seven stock markets, provides a better estimation in portraying stock return volatility than symmetric model. Moreover, the model can reveal the presence of asymmetric effects on those seven stock markets. Other finding shows that Hong Kong and Singapore markets play dominant roles in influencing volatility return of Indonesia’s stock market. In addition, the degree of interdependence between Indonesia’s and foreign stock market increased substantially after the 2007 global financial crisis, as indicated by a drastic increase of the impact of stock return volatilities in the US and UK market on the volatility of Indonesia’s stock return.

scholarly journals Stock Return Volatility And Trading Volume Relationships Captured With Stable Paretian GARCH And Threshold GARCH Models

2012 ◽  
Vol 11 (1) ◽  
pp. 47 ◽  
Author(s):  
Atsuyuki Naka ◽  
Ece Oral
Keyword(s):  
Stock Returns ◽  
Trading Volume ◽  
Stock Return ◽  
Goodness Of Fit ◽  
Garch Models ◽  
Return Volatility ◽  
Font Size ◽  
Gaussian Models ◽  
Stock Return Volatility ◽  
Dow Jones Industrial Average

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify;" class="MsoNormal"><span style="font-size: 10pt; mso-fareast-language: JA;"><span style="font-family: Times New Roman;">This paper examines the volatility of Dow Jones Industrial Average stock returns and the trading volume by employing stable Paretian GARCH and Threshold GARCH (TGARCH) models. Our results indicate that the trading volume significantly contributes to the volatility of stock returns. Additionally, strong leverage effects exist with negative shocks having a larger impact on volatility than positive shocks. The likelihood ratio tests and goodness of fit support the use of stable Paretian GARCH and TGARCH models over Gaussian models.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

Impact of Weather on Return and Volatility: Evidence from Indian Stock Market

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Vijayakumar N. ◽  
Dharani M. ◽  
Muruganandan S.
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Return ◽  
Return Volatility ◽  
Weather Factors ◽  
Stock Return Volatility ◽  
Daily Data ◽  
Indian Stock Market ◽  
The Impact

This study examines the impact of Weather factors on return and volatility of the Indian stock market. The study uses the daily data of top four metros and tests its impact on the return and volatility of S&P CNX Nifty index from January 2008 to December 2013. This study applies GARCH (1, 1) model and find that the stock returns are influenced by temperature in Chennai and the stock return volatility influenced by the temperature in Mumbai, Delhi and Kolkata.

scholarly journals Causality Relationship between Trading Volume and Stock Return: Evidence from Nepalese Stock Market

2016 ◽  
Vol 9 (1) ◽  
pp. 13-28
Author(s):  
Shivaram Shrestha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Price ◽  
Trading Volume ◽  
Stock Return ◽  
Causal Relation ◽  
Data Sets ◽  
Monthly Data ◽  
Trading Model ◽  
Causality Relationship

This paper aims to empirically examine the causal relation between trading volume and stock returns for Nepalese Stock Market using Garner causality procedure, using monthly data for the period of July 2007 to February 2015. The study analyzed for the investigation of the Granger causality between trading volume and stock price using monthly data sets to ascertain if the causality runs from volume to stock price or from stock price to volume or in both directions. This study detected unidirectional causality from stock returns to trading volume that is indicative of noise trading model of return volume interaction in this market.

Investor sentiment and stock return volatility: evidence from the Indian Stock Exchange

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sreenu N ◽  
Suresh Naik
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Return ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Return Volatility ◽  
Stock Market Volatility ◽  
Content Type ◽  
Stock Return Volatility

PurposeIn any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.Design/methodology/approachThe paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.FindingsFinally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.Practical implicationsThe investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.Originality/valueThe outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

scholarly journals The Sukuk Effect on Stock Return Volatility in Indonesia

2020 ◽  
Vol 3 (2) ◽  
pp. 125-153
Author(s):  
Legina Legina ◽  
Harjum Muharam ◽  
Ahmad Maulin Naufa
Keyword(s):  
Stock Returns ◽  
Trading Volume ◽  
Stock Return ◽  
Sampling Technique ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Finance Company ◽  
Egarch Model ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Observation Period

The purpose of this research is to examine the effect of the announcement of Sukuk issuance to stock return volatility and to examine the phenomenon of time the varying volatility that occurs in the movement of stock returns and volatility. The data used in this research are daily closing price and trading volume incorporate Sukuk issued during the year 2009-2013 in the D-100 D+100 of Observation period. Samples utilized the purposive sampling technique to obtain the Samples of 13 companies. This study uses EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) method of analysis. The results show that the best model for each sample in the EGARCH model is different. The results show that the phenomenon of time-varying volatility occurred in 13 samples. From 13 samples, event announcement of the Sukuk issuance does not affect the volatility of stocks returns except for Multi Adira Finance company. Furthermore, the trading volume affects the stock returns volatility on 9 companies, hence do not affect the other four companies.

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