Trading Volume and Market Volatility: Developed versus Emerging Stock Markets

Girard, Eric; Biswas, Rita

doi:10.1111/j.1540-6288.2007.00178.x

Cited by 89 publications

(50 citation statements)

References 77 publications

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“…19 Since realized kurtosis measures the occurrence of extreme returns (fat-tailedness), such increases may cause more clusters of return volatility, which last for longer periods of time. This result is consistent with Girard and Biswas (2007), who find that the volatility persistence is reduced after controlling for both expected and unexpected trading volume. Our finding can be fundamentally explained by a prominent result found in the trading volume -GARCH effect literature (initially reported in Lamoureux & Lastrapes, 1990), which indicates that trading volume is a source of heteroskedasticity (volatility clustering).…”

Section: Volume Impactssupporting

confidence: 94%

How does trading volume affect financial return distributions?

Xuan

Brooks

Treepongkaruna

et al. 2014

International Review of Financial Analysis

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We assess investors' reaction to new information arrivals in financial markets by examining the relationships between trading volume and the higher moments of returns in 18 international equity and currency markets. Our volume-volatility results support extant information theories and further contribute new evidence of cross market relations between volume and volatility. We also find that the direct impact of volume on the level of negative skewness is less significant for more diversified regional portfolios. Furthermore, the negative interaction between volume and kurtosis can be explained by the differences of opinion in financial markets. We observe stronger interdependence among higher moments in reaction to significant events, but the strength is dampened by trading volume. This result is consistent with trading volume being a source of heteroskedasticity in asset returns.

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Section: Volume Impactssupporting

confidence: 94%

How does trading volume affect financial return distributions?

Xuan

Brooks

Treepongkaruna

et al. 2014

International Review of Financial Analysis

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show abstract

“…For nonlinear causality, the results showed unidirectional Granger causality from volume to return. Girard and Biswas (2007) examined the relation between volatility and volume in 49 equity indexes covering developed and emerging markets. They found, based on TARCH model, the effect of volatility is different between developed and emerging markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Testing price volume relationships for Indian commodity futures

Biswas

Rajib

2011

Journal of Indian Business Research

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Purpose -The nature of price volume relationship in asset market has been an interesting subject in financial research as it reveals a very important aspect which has implications for market efficiency. The purpose of this paper is to examine price volume relationships in Indian commodity futures market. Design/methodology/approach -There are two competing models in price volume relationship. Mixture of distribution hypothesis, suggesting a positive contemporaneous relationship and sequential information arrival hypothesis (SIH), suggesting a positive intertemporal causal relationship. Both are tested using correlation coefficient and Granger causality test with vector auto regressive methodology. Findings -Though there exists contemporaneous correlation between volume and price change in some of the cases, but in general on the basis of the presence of Granger causality it follows that SIH is supported. Research limitations/implications -As only three commodities futures have been studied in this paper, this study can be extended to include more number of commodities currently being traded so as to make it more exhaustive. Practical implications -The research has been done with the data of MCX Gold, MCX Silver and MCX Crude. The results of causality suggest that inefficiency level is maximum in Silver which may be attributed to informational asymmetry. Originality/value -The Indian commodity futures market is of very recent origin. Hence, very little research work has been undertaken in this space. The paper presents an assessment of the existence of informational asymmetry among the three commodity futures under the study.

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“…From this, we obtain our total population of 825 completed, friendly mergers with a single publically-listed bidder. We use stratified random sampling to select 190 deals from this population, and we estimate firm-level volume-augmented 4 See, inter alia, Lamoureux and Lastrapes (1990a, 1990b, Richardson and Smith (1994), Andersen (1996), Bollerslev and Jubinski (1999), Gallo and Pacini (2000), Omran and McKenzie (2000), Lobato and Velasco (2000), Liesenfeld (2001), Regulez andZarraga (2002), Li and Wu (2006), Girard and Biswas (2007), Ané and Ureche-Rangau (2008), Darolles, Le Fol andMero (2009), Andersen et al (2010) and Fleming and Kirby (2011). 5 We should also observe no difference on average between the pre-announcement volume-volatility relations across different bid considerations.…”

Section: Introductionmentioning

confidence: 99%

“…See alsoLamoureux and Lastrapes (1994),Gallo and Pacini (2000),Omran and McKenzie (2000),Lobato and Velasco (2000),Regulez and Zarraga (2002),Luu and Martens (2003),Girard and Biswas (2007), Ané and Ureche-Rangau(2008), Darolles, Le Fol and Mero (2009), Ureche-Rangau and de Rorthays (2009), and Carroll and Kearney (2012) who review prior work.…”

mentioning

confidence: 95%