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Trading Volume in Dealer Markets

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  • Malinova, Katya
  • Park, Andreas

Abstract

We develop a financial market trading model in the tradition of Glosten and Milgrom (1985) that allows us to incorporate nontrivial 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.

Suggested Citation

  • Malinova, Katya & Park, Andreas, 2010. "Trading Volume in Dealer Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(6), pages 1447-1484, December.
    • Handle: RePEc:cup:jfinqa:v:45:y:2011:i:06:p:1447-1484_00
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    References listed on IDEAS

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    Cited by:

    1. Andrikopoulos, Andreas, 2015. "Truth and financial economics: A review and assessment," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 186-195.
    2. Katya Malinova & Andreas Park, 2009. "Intraday Trading Patterns: The Role of Timing," Working Papers tecipa-365, University of Toronto, Department of Economics.
    3. Palani-Rajan Kadapakkam & Timothy Krause & Yiuman Tse, 2013. "Exchange Traded Funds, Size-Based Portfolios, And Market Efficiency," Working Papers 0214fin, College of Business, University of Texas at San Antonio.
    4. Kendall, Chad, 2018. "The time cost of information in financial markets," Journal of Economic Theory, Elsevier, vol. 176(C), pages 118-157.
    5. Palani-Rajan Kadapakkam & Timothy Krause & Yiuman Tse, 2015. "Exchange traded funds, size-based portfolios, and market efficiency," Review of Quantitative Finance and Accounting, Springer, vol. 45(1), pages 89-110, July.
    6. Katarzyna Bien-Barkowska, 2012. ""Does it take volume to move fx rates?" Evidence from quantile regressions," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 12, pages 35-52.
    7. Abhinava Tripathi, 2021. "The Arrival of Information and Price Adjustment Across Extreme Quantiles: Global Evidence," IIM Kozhikode Society & Management Review, , vol. 10(1), pages 7-19, January.
    8. Malinova, Katya & Park, Andreas, 2014. "The impact of competition and information on intraday trading," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 55-71.
    9. Louhichi, Waël, 2011. "What drives the volume-volatility relationship on Euronext Paris?," International Review of Financial Analysis, Elsevier, vol. 20(4), pages 200-206, August.

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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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