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 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 Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market

2018 ◽  
Vol 10 (10) ◽  
pp. 3361 ◽  
Author(s):  
Junru Zhang ◽  
Hadrian Djajadikerta ◽  
Zhaoyong Zhang
Keyword(s):  
Stock Returns ◽  
Portfolio Management ◽  
Stock Return ◽  
Return Volatility ◽  
Chinese Stock Market ◽  
Stock Return Volatility ◽  
News Release ◽  
Volatility Persistence ◽  
Google Search ◽  
The Impact

This paper examines the impact of firms’ sustainability engagement on their stock returns and volatility by employing the EGARCH and FIGARCH models using data from the major financial firms listed in the Chinese stock market. We find evidence of a positive association between sustainability engagement and stock returns, suggesting firms’ sustainability news release in favour of the market. Although volatility persistence can largely be explained by news flows, the results show that sustainability news release has the significant and largest drop in volatility persistence, followed by popularity in Google search engine and the general news. Sustainability news release is found to affect positively stock return volatility. We also find evidence that market expectation can be driven by the dominant social paradigm when sustainability is included. These findings have important implications for market efficiency and effective portfolio management decisions.

Capital Gains Taxes and Stock Return Volatility

2013 ◽  
Vol 35 (2) ◽  
pp. 1-31 ◽  
Author(s):  
Zhonglan Dai ◽  
Douglas A. Shackelford ◽  
Harold H. Zhang
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Cash Flows ◽  
Capital Gains ◽  
Return Volatility ◽  
Capital Gains Tax ◽  
Tax Rate ◽  
Stock Return Volatility ◽  
Capital Gains Taxes ◽  
The Impact

ABSTRACT This paper presents an empirical investigation of the impact of capital gains taxes on stock return volatility. We predict that the more stock returns are subject to capital gains taxation, the greater the increase in return volatility following a capital gains tax rate cut due to reduced risk-sharing in firms' cash flows between shareholders and the government. Consistent with this prediction, we find larger increases in the return volatility for more appreciated stocks than for less appreciated stocks and for non-dividend-paying stocks than for dividend-paying stocks after both 1978 and 1997 capital gains tax rate reductions. The findings imply that capital gains taxes convey a heretofore overlooked benefit of lower stock return volatility.

THE IMPACT OF STOCK RETURNS VOLATILITY ON CREDIT DEFAULT SWAP RATES: A COPULA STUDY

2005 ◽  
Vol 08 (08) ◽  
pp. 1135-1155 ◽  
Author(s):  
FATHI ABID ◽  
NADER NAIFAR
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Credit Default Swap ◽  
Archimedean Copula ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Credit Default ◽  
Default Swap ◽  
The Impact ◽  
Swap Rates

The aim of this paper is to study the impact of stock returns volatility of reference entities on credit default swap rates using a new dataset from the Japanese market. The majority of empirical research suggests the inadequacy of multinormal distribution and then the failure of methods based on correlation for measuring the structure of dependency. Using a copula approach, we can model the different relationships that can exist in different ranges of behavior. We study the bivariate distributions of credit default swap rates and the measure of stock return volatility estimated with GARCH (1,1) and focus on one parameter Archimedean copula. Starting from the empirical rank correlation statistics (Kendall's tau and Spearman's rho), we estimate the parameter values of each copula function presented in our study. Then, we choose the appropriate Archimedean copula that better fit to our data. We emphasize the finding that pairs with higher rating present a weaker dependence coefficient and then, the impact of stock return volatility on credit default swap rates is higher for the lowest rating class.

Macroeconomic Sensitivity, Risk-Return Trade-Off and Volatility Dynamics Evidence From Developed and Developing Markets

2019 ◽  
Vol 6 (1) ◽  
pp. 1-16
Author(s):  
Faisal Khan ◽  
Hashim Khan ◽  
Saif Ur-Rehman Khan ◽  
Muhammad Jumaa ◽  
Sharif Ullah Jan
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Stock Exchange ◽  
Three Dimensions ◽  
Leverage Effect ◽  
Return Volatility ◽  
Macroeconomic Factors ◽  
Stock Return Volatility ◽  
Risk Return ◽  
The Impact

This study aims to examine the impact of macroeconomic factors on the stock return volatility along with the pricing of risk, and asymmetry and leverage effect on a comparative basis for the USA and UAE markets. Further, these three dimensions are also investigated with regard to various firm's features (such as firm's size and age). The daily data for the period 4th January 2010 to 29th December 2017 of firm stock returns from the New York Stock Exchange (NYSE), the Abu Dhabi Securities Exchange (ADSE), and the Dubai Financial Market (DFM) is considered and three time-series models were applied. The results from GARCH (1. 1) indicated that all the economic factors have significant impact on the stock return volatility in both the markets. Similarly, the study also found evidence of asymmetry & leverage effect using EGARCH in the NYSE (for all firms) and the UAE (partially). Finally, for a majority of the firms, a positive risk-return relationship is found in the UAE and a negative risk-return relationship is found in the NYSE using GARCH-in the mean. Interestingly, these results in context of both markets were different with respect to various firm features such as firm size and age. In light of these results, it is concluded that both the markets have different dynamics with regard to all three dimensions. Hence, the investors have a clear opportunity to diversify their risk and investments across developed and emerging markets.

scholarly journals Impact of M&A Announcement on Stock Returns of Acquirer Companies: New Evidence from Indian Stock Market

2017 ◽  
Vol 3 (1) ◽  
pp. 86
Author(s):  
Richa Vij
Keyword(s):  
Stock Market ◽  
Mergers And Acquisitions ◽  
Stock Returns ◽  
Capital Market ◽  
Stock Return ◽  
Strong Form ◽  
Business Objectives ◽  
Indian Stock Market ◽  
The Impact ◽  
New Evidence

Mergers and Acquisitions (M&As) are often used as preferred tools of corporate structuring to serve a variety of business objectives and add value for the shareholders. Earlier studies have triggered a number of questions regarding the impact of M&As for the shareholders of acquiring companies. This paper focuses on the M&A among Indian companies and the response of the Indian capital market to such attempts as reflected in the changes in the stock return for different window periods close the M&A announcement. The findings of the present study suggest that there is significant impact of M&A announcement on stock returns for almost half of the sample acquirer companies. The study offers evidence in support of the contention that Indian stock market is not efficient in the semi-strong form with respect to M&A announcement information for acquirer companies and emphasizes that  investment analyst cannot ignore the information regarding the M&A deals.

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.

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.

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

2021 ◽  
pp. 031289622110102
Author(s):  
Mousumi Bhattacharya ◽  
Sharad Nath Bhattacharya ◽  
Sumit Kumar Jha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
High Frequency ◽  
Jel Classification ◽  
Exogenous Shock ◽  
Intraday Data ◽  
Day Of The Week ◽  
Holiday Effects ◽  
Indian Stock Market ◽  
The Impact

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

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