Testing the mixture of distributions hypothesis on target stocks

Carroll, Rachael; Kearney, Colm

doi:10.1016/j.intfin.2015.05.003

Cited by 14 publications

(4 citation statements)

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“…In the recent studies, Omid Sabbaghi (2011), Henryk Gurgul and Robert Syrek (2013), and Rachael Carroll and Colm Kearney (2015) found evidence to support the relationship between a contemporaneous volume and volatility in the developed markets. Consistent with the prior findings, a positive relationship between trading volume and volatility is also observed in the emerging markets (Cathy Ning and Tony S. For the Middle East and North Africa (MENA) region, Berna Okan, Onur Olgun, and Sefa Takmaz (2009) examined the relationship between volume and volatility for the Istanbul Stock Exchange (ISE)-30 futures index using daily data by applying GARCH, EGARCH and VAR models.…”

Section: Literature Reviewmentioning

confidence: 96%

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

Ezzat

Kirkulak-Uludag

2017

Panoeconomicus

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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.

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Section: Literature Reviewmentioning

confidence: 96%

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

Ezzat

Kirkulak-Uludag

2017

Panoeconomicus

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“…1. Most of the research on the price–volume relation is concerned about mixture of distribution hypothesis (MDH) which states that volatility and trading volume are positively related implying that volumes and volatility are both derived from the same unobservable mixing variable, the rate of information flow to the market (Harris, 1986; Karpoff, 1987; Ciner, 2002; Ané and Ureche-Rangau, 2008; Chen, 2012; Carroll and Kearney, 2015). The second theory, the sequential arrival of information (SAI), states that past prices and volumes can predict each other as the market participants react to new information sequentially (Copeland, 1976; Chen and Daigler, 2008; Mougoué and Aggarwal, 2011).…”

Section: Notesmentioning

confidence: 99%

The relationship between trading volume and exchange rate volatility: linear or nonlinear?

Kumar

2019

IJMF

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Purpose The purpose of this paper is to examine the linear and nonlinear relations between returns volatility and trading volume for the Indian currency futures market. Design/methodology/approach To examine the contemporaneous relation between returns volatility and volume, the author uses the generalized method of moment estimator. For the linear causal relation, the author makes use of Granger (1969) bivariate vector autoregression model. The author tests for nonlinear Granger causality between returns volatility and trading volume based on a modified version of the Baek and Brock (1992) nonparametric technique developed by Hiemstra and Jones (1994). Findings The results indicate a negative contemporaneous relation between returns volatility and trading volume; therefore, the mixture of distribution hypothesis is not supported. The results of both linear and nonlinear Granger causality between futures returns volatility and trading volume indicate a significant bidirectional relation between the two variables lending support to the sequential arrival of information hypothesis. The results are robust to divergence of opinions as proxied by open interest. Practical implications The findings of this paper are important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant causal relation between returns volatility and trading volume implies that trading volume helps predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Furthermore, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Originality/value To the best of the author’s knowledge, there is no study that investigates the forecast ability of trading volume to futures returns volatility in an emerging currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price–volume relation in the Indian currency futures market.

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“…M&A announcements are events that incorporate new information about firms that are directly or indirectly involved in this process. This information modifies both the return and the volatility of firms (Carroll & Kearney, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Stock Market Reaction to Mergers and Acquisitions: Evidence from the Banking Industry

Lozada

Cortés

Velásquez-Gaviria

2021

Latin American Business Review

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Mergers and acquisitions (M&As) are mainly a mechanism used in the Latin American banking industry to carry out business consolidation. This paper focuses on the effect of M&A announcements on stocks of Latin American banks and their rivals between 2000 and 2019. We evaluate two impacts of M&A announcements: impacts on cumulative abnormal returns (CAR) and impacts on event-induced variance (EIV). We use the GARCH-based event-study method. We find that acquirers and target banks have a statistically significant CAR, however, the sign is inconclusive. Rivals of acquirers and targets are not affected by M&A announcements. In general, we observe that EIV is negative for acquirers, targets, and rivals. Finally, we estimate a multivariate GARCH model to isolate the effects of co-movements of volatility between the acquirer and the target, and we find that the results remain qualitatively equal.

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Testing the mixture of distributions hypothesis on target stocks

Cited by 14 publications

References 68 publications

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

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

The relationship between trading volume and exchange rate volatility: linear or nonlinear?

The Stock Market Reaction to Mergers and Acquisitions: Evidence from the Banking Industry

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