What drives the volume–volatility relationship on Euronext Paris?

Louhichi, Waël

doi:10.1016/j.irfa.2011.03.001

Cited by 33 publications

(19 citation statements)

References 45 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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“…In general, our results indicate that the inclusion of ARMA effects in the mean equation, the extra lags in the variance equation to come up with a better fit for some of the markets, and the trading volume variables have contributed to the reduced volatility persistence. This result is consistent with the results of some of the previous studies which argued that the more variables added to the model the less the value of the volatility persistence (see for example Lamoureux & Lastrapes, 1990, Chandra Pati & Rajib, 2010, Louhichi, 2011 but contradicts with the results found by Naik & Padhi (2014) and Naik et al (2018). ISSN 1923-3981 E-ISSN 1923-399X The proposition that the crisis has motivated new regulations and reforms leading to lower volatility (as measured by the persistence value) is confirmed only for the Jakarta stock market.…”

Section: Autocorrelationsupporting

confidence: 92%

Have Stock Markets Become Less Volatile After the Great Recession?

Aldeehani¹

2019

RWE

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This paper investigates volatility modeling in light of the 2008 global financial crisis. The study was motivated by the measures and regulations introduced by most of the countries following the shock to stabilize their financial markets. The theoretical proposition is that these measures should succeed in reducing volatility which would be modeled differently following the crisis. The adopted ARMA-GARCH process included positive and negative trading volume change to capture the asymmetric effect of trading volume on market volatility for seven international markets. The results indicate that the majority of these markets were not so successful in reducing volatility following the crisis. There is evidence of volatility persistence which dissipates very quickly. Although volatility is modeled differently before and after the crisis, each market is modeled uniquely. The effect of trading volume was found to be asymmetric. Only positive change was a valid predictor. Detailed discussions of the results, implications, and recommendations are provided. JEL Classification: G01, G21, G32In this paper, we aim to explore this conceptual proposition in the context of the 2008 great recession. We seek answers to particular questions relevant to this proposition. Assuming that the stock markets are being better-controlled and regulated following the crisis, the important question is, have these markets become less volatile? Whether the proposition is confirmed or not, how can volatility be modeled? Is it going to be different? In other words, are the determinants of volatility still the same after the crisis?Answers to these questions should contribute more to our understanding of the effect of major financial crises on stock markets volatility and for how long it persists. The results of this study should tell us if the evidence can be generalized for all stock markets.In the next section, the relevant literature will be discussed with the objective to derive the research conceptual framework and hypotheses. An overview of regulatory measures and reforms, following the 2008 crisis in the selected sample stock markets, will be presented. This is followed by a section on the data and methodology where we discuss the scope of our sample stock markets and the method of estimation. The results of the research and discussion will then be presented. The paper ends with a section on the concluding remarks where we highlight the main contribution of the paper, along with the limitations, implications, and recommendations. Literature ReviewIn statistics, volatility in financial data time series can be defined as the dispersion of rate of returns, typically, measured by the standard deviation or variance. The higher the value of the measure the more volatile (risky) the asset. When dealing with causality, researchers, typically apply regression methods. These methods assume that data should be normally distributed. The nature of the financial data, however, is not. They also assume that the variance of the error term should be homosc...

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“…contains concerns the fact that participants in the Ukrainian stock market view the US economy as the most important source of macroeconomic information, confirming the leading role of the US economy on a global scale.Based on the use of daily quotations, it has been established that the reaction of the Ukrainian stock market volatility under the influence of United States non-monetary information signals is not short-term, it continues even after the end of trading. This result is to some extent consistent with the findings ofErrunza and Hogan (1998) andLouhichi (2011) who note that the effect of non-monetary information signals of the United States on the volatility of equity markets in economically developed countries lasts up to two months.…”

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confidence: 91%

Influence of non-monetary information signals of the USA on the Ukrainian stock market volatility

Pavlov

Pavlova

Lemberg

et al. 2019

Investment Management and Financial Innovations

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The Ukrainian PFTS stock index volatility reaction as a whole and its constituent economic sectors (“Basic Materials”, “Financials”, “Industrials”, “Oil & Gas”, “Telecommunications”, “Utilities”) to seven non-monetary US information signals (“Consumer price index”, “Personal spending”, “Unemployment rate”, “Gross domestic product”, “Industrial production”, “Consumer confidence”, “Housing starts”) was carried out for the period 2000–2017 on the basis of closing stock quotations in the trading day format. To assess the “surprise” component direct influence nature of the USA selected non-monetary information signals on the PFTS stock index, an AR-GARCH econometric modelling device was used. The results achieved clearly indicate the presence of some PFTS stock index economic sectors heterogeneous reaction to the United States individual non-monetary information signals announcement. For example, such economic sectors as “Basic Materials”, “Financials”, and “Oil & Gas” volatility response to the US non-monetary information signal “Consumer price index” “surprise” components the opposite of the overall PFTS stock index reaction. It can also be concluded that the United States non-monetary information signals influence on the Ukrainian stock market volatility depends not only on the financial cycle phase and data frequency, but also on the PFTS stock index economic sector.

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What drives the volume–volatility relationship on Euronext Paris?

Cited by 33 publications

References 45 publications

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Have Stock Markets Become Less Volatile After the Great Recession?

Influence of non-monetary information signals of the USA on the Ukrainian stock market volatility

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