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

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  • Stanislav Anatolyev

    ()
    (NES)

  • Dmitry Shakin

Abstract

We investigate the distribution and evolution of intertrade durations for frequently traded stocks at the Moscow Interbank Currency Exchange. We use a flexible econometric model based on ARMA and GARCH 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. We also analyze what factors determine the dynamics of logdurations and in which way. 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.

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Bibliographic Info

Paper provided by Center for Economic and Financial Research (CEFIR) in its series Working Papers with number w0070.

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Length: 37 pages
Date of creation: Aug 2006
Date of revision:
Handle: RePEc:cfr:cefirw:w0070

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Keywords: High frequency data; Trading intensity; Intertrade durations; ACD model; ARMA–GARCH model; Market microstructure.;

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References

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Citations

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Cited by:
  1. Zhi-Qiang Jiang & Wei Chen & Wei-Xing Zhou, 2008. "Detrended fluctuation analysis of intertrade durations," Papers 0806.2444, arXiv.org.
  2. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
  3. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
  4. 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.
  5. 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.
  6. Georges Dionne & Pierre Duchesne & Maria Pacurar, 2005. "Intraday Value at Risk (IVaR) Using Tick-by-Tick Data with Application to the Toronto Stock Exchange," Cahiers de recherche 0533, CIRPEE.
  7. 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.

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