Stock Prices and Volume

Gallant, A. Ronald; Rossi, Peter E.; Tauchen, George

doi:10.1093/rfs/5.2.199

Cited by 1,161 publications

(638 citation statements)

References 55 publications

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“…Mei et al 12 found that trading caused by investors' speculative motives could explain a significant fraction of the price difference between the dual-class shares. Gallant et al 13 investigated the price and volume co-movement using daily data from 1928 to 1987 for the New York stock exchange and found positive correlation between conditional volatility and volume. Griffin et al 14 investigated the dynamic relation between market-wide trading activity and returns in 46 markets and reported a strong positive relationship between turnover and past returns.…”

Section: Volume and Stock Price Movementsmentioning

confidence: 99%

The impact of trading volume on stock price volatility in the Arab economy

Sabri

2008

J Deriv Hedge Funds

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This study intends to examine the price-volume movements in the Arab stock markets, in order to determine the impact of changes in trade volume on the volatility of stock prices as expressed by the unified AMF stock price index. The research covers a sample of eight out of the 15 Arab stock markets included in the Arab Monetary Fund database, using monthly data from 1994 to 2006. The study found that there is an increase in both trading volume and stock price volatility, which may be considered a recent phenomenon in the majority of the Arab stock markets. The study also found that the volume-stock price movements are significantly integrated for all selected markets, while the highest (to lowest) correlation coefficient between volume and stock price movement was found in the Saudi stock market, Amman stock market, Muscat stock market, and Kuwait stock market (in that order). Finally, the correlation between volume and price movement is higher in the stock markets of the oil Arab states compared to the nonoil Arab states.

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Section: Volume and Stock Price Movementsmentioning

confidence: 99%

The impact of trading volume on stock price volatility in the Arab economy

Sabri

2008

J Deriv Hedge Funds

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“…The performance of the MCL estimator is further illustrated by fitting the model to the seasonally adjusted (Gallant et al, 1992) S & P 500 returns. JPR and Danielsson (1994b) has already utilized a subset of the data (2/1/80-30/12/87, ¹"2,022 observations) to fit the basic SV model.…”

Section: S and P500 Return Seriesmentioning

confidence: 99%

“…For ease of reference the results of the basic SV are reproduced in the upper panel. The Gallant et al (1992). The sample length is ¹"2022 observations.…”

Section: Extensions Of the Basic Sv Model (I): Heavy ¹Ailsmentioning

confidence: 99%

Estimation of stochastic volatility models via Monte Carlo maximum likelihood

Sandmann¹,

Koopman²

1998

Journal of Econometrics

259

215

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This paper discusses the Monte Carlo maximum likelihood method of estimating stochastic volatility (SV) models. The basic SV model can be expressed as a linear state space model with log chi-square disturbances. The likelihood function can be approximated arbitrarily accurately by decomposing it into a Gaussian part, constructed by the Kalman filter, and a remainder function, whose expectation is evaluated by simulation. No modifications of this estimation procedure are required when the basic SV model is extended in a number of directions likely to arise in applied empirical research. This compares favorably with alternative approaches. The finite sample performance of the new estimator is shown to be comparable to the Monte Carlo Markov chain (MCMC) method.1998 Elsevier Science S.A. All rights reserved.JEL classification: C15; C22

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“…Proposition 5 has been empirically documented in several studies, see, for instance, Gallant, Rossi, and Tauchen (1992) or Karpoff (1987) for earlier references. Chordia, Roll, and Subrahmanyam (2002) and Chan and Fong (2000) additionally highlight the effect of the order imbalance on price variability, providing evidence for both Propositions 4 and 5.…”

Section: Proposition 5 (Price Variability and Volume)mentioning

confidence: 92%

“…Proposition 4 order imbalance ր average absolute price change ր Chordia, Roll, and Subrahmanyam (2002), Chan and Fong (2000) Proposition 5 volume ր average absolute price change ր Chordia, Roll, and Subrahmanyam (2002), Chan and Fong (2000), Gallant, Rossi, and Tauchen (1992) Proposition 7 entry rate ր average volume ր average order imbalance ր average price changes ր duration ց causes for changes in entry rate: index inclusion, international market opening financial deregulation, cross-listing Propositions 8 & 9 information quality ր price impact ր average volume ր average order imbalance ր average price changes ր duration ց , Roll, and Subrahmanyam (2008), causes for changes in information quality: new disclosure rules, changes in analyst coverage, changes in information technology, changes in ownership structure (hedge funds rather than mutual funds), development of a new analysis tool, improvements in analysts' earnings forecasts Proposition 10 informed trading ր average volume ց duration ր (numerically) price changes ր causes for changes in the proportion of informed trading: changes in ownership structure (hedge funds rather than retail), market changes that trigger more retail participation or day trading …”

Section: Claim (Ii)mentioning

confidence: 99%

Trading Volume in Dealer Markets

Malinova

Park

2010

J. Financ. Quant. Anal.

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We develop a financial market trading model in the tradition of Glosten and Milgrom (1985) that allows us to incorporate non-trivial volume. We observe that in this model price volatility is positively related to the trading volume and to the absolute value of the net order flow, i.e. the order imbalance. Moreover, higher volume leads to higher order imbalances. These findings are consistent with well-established empirical findings. Our model further predicts that higher trader participation and systematic improvements in the quality of traders' information lead to higher volume, larger order imbalances, lower market depth, shorter duration, and higher price volatility. * Malinova, katya.malinova@utoronto.ca, +1 416 978 5283; Park, andreas.park@utoronto.ca, +1 416 978 4189; University of Toronto, 150 St. George Street, Max Gluskin House, Toronto, M5S 3G7, ON, Canada. We thank Lones Smith as well as Ryan Davies, Rob McMillan, Jim Pesando, Carolyn Pitchik, and seminar participants at the 2007 CEA meetings and the University of Michigan for helpful discussions and comments. We are especially grateful to Hank Bessembinder (the editor) and Ohad Kadan (the referee) for detailed comments and suggestions. Financial support from the SSHRC and the Connaught Foundation is gratefully acknowledged. All errors remain our responsibility. I IntroductionAn important empirical regularity is that price volatility is positively related to trading volume and to the order imbalance (i.e. the absolute difference between the volumes of buy and sell orders); see Chordia, Roll, and Subrahmanyam (2002) for a comprehensive list of references or Karpoff (1987) for earlier studies. Volume is generated by trading activity, which is commonly explained by diversification and hedging motives, liquidity needs, or asymmetric information. Order imbalances occur either by chance or because of diverging opinions, which are often the result of heterogenous information.Focussing on the information motive, we develop a model to study the impact of nontrivial volume on prices. A key feature of our model is the parsimonious formulation of traders' information. It allows us to study how changes in the underlying information environment influence market activity. The economic questions that can then be addressed range from the impact of regulatory changes, such as regulation Fair Disclosure, to the effect of improvements in data processing technology. For instance, do such changes increase or decrease price volatility and do they lead to more or less trading?Our model combines features of Glosten and Milgrom (1985) and Kyle (1985) and has the following structure. Liquidity is supplied by an uniformed, risk-neutral and competitive market maker (or dealer). Demanders of liquidity either trade for reasons outside the model (e.g., to rebalance their portfolio or inventory), or they have private information about the fundamental value of the security. Specifically, informed traders receive private binary signals of heterogenous precisions, or qualities. ...

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Stock Prices and Volume

Cited by 1,161 publications

References 55 publications

The impact of trading volume on stock price volatility in the Arab economy

The impact of trading volume on stock price volatility in the Arab economy

Estimation of stochastic volatility models via Monte Carlo maximum likelihood

Trading Volume in Dealer Markets

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