New Evidence on the Relation between Trading Volume and Volatility

Çelik, Sibel

doi:10.5296/ber.v3i1.3222

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…These results consistent with results of a many studies, for instance Lamoureux and Lastrapes (1990), Pyun 2000, Huang and Yang (2001), Bohl and Henke (2003), Kumar et al (2010), Louhichi (2011), and Celik (2013). In emerging markets Huang and Yang (2001) have found similar results in Taiwan.…”

Section: The Results Presents Insupporting

confidence: 89%

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Ananzeh¹

2015

International Journal of Financial Research

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This paper investigates the empirical relationship between trading volume and conditional volatility using data from Amman Stock Exchange (ASE) within the framework of Mixed Distribution Hypothesis (MDH). Our sample covered 27 securities, which is most active stocks traded for the period span from 2002 to 2012. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) k Exchange model employed in order to test the persistence in the volatility of stock returns. Our results confirm positive and strong relationship between trading volume for individual stocks and conditional volatility of returns. Moreover, the degree of volatility persistence reduced through the process of adding the contemporaneous volume into the conditional variance equation of GARCH model, and this is according to the predictions of the Mixture of Distributions Hypothesis (MDH).

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Section: The Results Presents Insupporting

confidence: 89%

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Ananzeh¹

2015

International Journal of Financial Research

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“…These results support works of Lee and Rui (2002), Majdosz (2003, 2005), Kumar, Singh, and Pandey (2009), Wang (2012), Chuang, Liu, andSusmel (2012), Celik (2013), Davidsson (2014)... This contemporaneous relationship is mainly due to the joint dependence of volume and volatility to the information arrival rate as predicted by the Mixture of Distribution Hypothesis (MDH) assuming that the dissemination of the new information is contemporaneous and that the instant reaction of the different actors regarding its arrival on the market implies a positive contemporaneous relationship between volume and volatility where the current value of trading volume could explain the current price volatility.…”

Section: Wwwccsenetorg/ibrsupporting

confidence: 89%

“…For other shares, this positive impact of the volume is not significant. Given that price volatility is highly influenced by information flow (Lamoureux & Lastrapes, 1990;Kalev, Liu, Pham, & Jarnecic, 2004;Huang & Wang, 2012;Celik, 2013;…), the significance of volume impact on volatility could be explained by the information content of trading activity. Indeed, price volatility is a manifestation of the temporal variability of information flow whose volume is a proxy, so that the days including several information would be associated with an important trading volume and thus large price fluctuations, and that the slow information flow could reduce both trade and price movements.…”

Section: Contemporaneous Volatility-volume Relationshipmentioning

confidence: 99%

“…Empirically a large number of empirical studies have documented a strong positive contemporaneous relationship between return volatility and trading volume (Jones, Kaul, & Lipson, 1994;Lee & Rui, 2002;Huang & Masulis, 2003;Alsubaie & Najand, 2009;Mahajan & Singh, 2009;Kumar, Singh, & Pandey, 2009;Giot, Laurent, & Petitjean, 2010;Kao & Fung, 2012;Chuang, Liu, & Susmel, 2012;Huang & Wang, 2012;Celik, 2013;Davidsson, 2014;Shahzed et al, 2014;…). However, despite the diversity of these studies, there was no general consensus regarding the component of trading volume that best explains price volatility and drives, therefore, the positive contemporaneous volatility-volume relationship.…”

Section: Introductionmentioning

confidence: 99%

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Number of Transactions, Trade Size and the Volume-Volatility Relationship: An Interday and Intraday Analysis on the Tunisian Stock Market

Belhaj¹,

Abaoub²,

Mahjoubi³

2015

IBR

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The purpose of this paper is to study the relationship between trading volume and unconditional price volatility on the Tunisian Stock Market in order to provide an empirical support to either the hypothesis of the strategic asymmetric information models or the hypothesis of competitive asymmetric information Models. More specifically, it aims to test the volume-volatility relationship and identify the component of trading volume (number of transactions or trade size) that explains more price volatility and drives this relationship. Our empirical tests are based on daily and intraday data related to the 43 most active and dynamic listed stocks on the Tunisian Stock Market from 02 January 2008 to 29 June 2012 for the daily analysis and from 03 October 2011 to 28 September 2012 for the intraday data. Our empirical analysis reveals several results. First, we confirm the strong positive contemporaneous relationship between trading volume and unconditional price volatility similarly to the Mixture of Distribution Hypothesis (MDH). Second, we show that whatever the frequency of used data, daily or intraday, the number of transactions is much more significant than the trade size in the explanation of the volatility and seems to be the dominant factor that drives the positive volume-volatility relationship. Third, we find that trade size has no explanatory power of the volatility beyond that the number of transactions and the positivity of the volume-volatility relationship reflects simply the positive relationship between number of transactions and volatility. Overall, our empirical results support the hypothesis of the strategic models and challenge the hypothesis of competitive models.Keywords: trading volume, unconditional volatility, number of transactions, trade size, volme-volatility relationship, information flow, mixture of distribution hypothesis, competitive asymmetric information models, strategic asymmetric information models

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“…They reported findings consistent with Sequential Information Arrival Hypothesis (SIAH) and rejected the MDH for the ISE-30 futures index. Sibel Celik (2013) examined the relationship between trading volume and return volatility within the scope of the MDH and the SIAH using intraday data from the ISE. He divided the data into two sub-samples in order to consider the effect of the global sub-prime crisis.…”

Section: Literature Reviewmentioning

confidence: 99%

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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New Evidence on the Relation between Trading Volume and Volatility

Cited by 14 publications

References 25 publications

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Number of Transactions, Trade Size and the Volume-Volatility Relationship: An Interday and Intraday Analysis on the Tunisian Stock Market

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

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