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Trade intensity in the Russian stock market: dynamics, distribution and determinants

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  • Stanislav Anatolyev
  • Dmitry Shakin

Abstract

The distribution and evolution of intertrade durations for frequently traded stocks at the Moscow Interbank Currency Exchange are investigated. A flexible econometric model based on ARMA and GARCH is used which, when coupled with a certain class of distributions that allow for skewness and slim-tailedness, adequately captures the characteristics of conditional distribution of durations for Russian stocks, and is able to generate high quality density forecasts. What factors determine the dynamics of log-durations, and in which way, are also analyzed. The results in particular indicate that the Russian market is characterized by aggressive informed traders and timid liquidity traders, and that the participants react evenly to upward and downward short-run price trends.

Suggested Citation

  • Stanislav Anatolyev & Dmitry Shakin, 2007. "Trade intensity in the Russian stock market: dynamics, distribution and determinants," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 87-104.
    • Handle: RePEc:taf:apfiec:v:17:y:2007:i:2:p:87-104
    DOI: 10.1080/09603100600606123
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    Cited by:

    1. Stanislav Anatolyev, 2013. "Objects of nonstructural time series modeling (in Russian)," Quantile, Quantile, issue 11, pages 1-12, December.
    2. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2009. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 777-792, December.
    3. Jiang, Zhi-Qiang & Chen, Wei & Zhou, Wei-Xing, 2009. "Detrended fluctuation analysis of intertrade durations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 433-440.
    4. Ruan, Yong-Ping & Zhou, Wei-Xing, 2011. "Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1646-1654.
    5. Anatolyev, Stanislav, 2008. "A 10-year retrospective on the determinants of Russian stock returns," Research in International Business and Finance, Elsevier, vol. 22(1), pages 56-67, January.
    6. Dionne, Georges & Pacurar, Maria & Zhou, Xiaozhou, 2015. "Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 202-219.
    7. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    8. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    9. Alexander Muravyev, 2009. "Dual Class Stock in Russia: Explaining a Pricing Anomaly," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 45(2), pages 21-43, March.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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