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The logarithmic ACD model: The microstructure of the German and Polish stock markets

Author

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  • Henryk Gurgul

    (AGH University of Science and Technology, Department of Applications of Mathematics in Economics)

  • Robert Syrek

    (Jagiellonian University in Kraków, Institute of Economics, Finance and Management)

Abstract

The main goal of this paper is to compare the microstructure of selected stocks listed on theFrankfurt and Warsaw Stock Exchanges. We focus on the properties of duration on both markets and on fitting the appropriate ACD models. Because of the quite different levels of capitalization of stocks on these markets, we observe essential discrepancies between these stocks. Whilefor most German companies on the DAX30, the Burr distribution fits better than generalized gamma distribution, the latter distribution is superior in the case of the largest Polish companies. Analyzing series by hazard function, we note the similarity of hazard functions for companies on both markets, which tend to have a U-shaped pattern.

Suggested Citation

  • Henryk Gurgul & Robert Syrek, 2016. "The logarithmic ACD model: The microstructure of the German and Polish stock markets," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 17(1), pages 77-92.
    • Handle: RePEc:agh:journl:v:17:y:2016:i:1:p:77-92
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    File URL: https://journals.agh.edu.pl/manage/article/view/2109/1546
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    References listed on IDEAS

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    1. Michael Goldstein & Gregory Laughlin & Anthony Aguirre & Joseph Grundfest, 2014. "Information Transmission between Financial Markets in Chicago and New York," The Financial Review, Eastern Finance Association, vol. 49(2), pages 283-312, May.
    2. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    3. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," LIDAM Discussion Papers CORE 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Engle, Robert F. & Russell, Jeffrey R., 1997. "Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 187-212, June.
    5. Richard B. Olsen & Ulrich A. Müller & Michel M. Dacorogna & Olivier V. Pictet & Rakhal R. Davé & Dominique M. Guillaume, 1997. "From the bird's eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets (*)," Finance and Stochastics, Springer, vol. 1(2), pages 95-129.
    6. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    7. Luc Bauwens & Pierre Giot, 2000. "The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks," Annals of Economics and Statistics, GENES, issue 60, pages 117-149.
    8. Hasbrouck, Joel & Saar, Gideon, 2009. "Technology and liquidity provision: The blurring of traditional definitions," Journal of Financial Markets, Elsevier, vol. 12(2), pages 143-172, May.
    9. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    10. James J. Angel & Lawrence E. Harris & Chester S. Spatt, 2011. "Equity Trading in the 21stCentury," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 1-53.
    11. Brogaard, Jonathan & Garriott, Corey, 2019. "High-Frequency Trading Competition," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(4), pages 1469-1497, August.
    12. Bauwens, Luc & Veredas, David, 2004. "The stochastic conditional duration model: a latent variable model for the analysis of financial durations," Journal of Econometrics, Elsevier, vol. 119(2), pages 381-412, April.
    13. repec:adr:anecst:y:2000:i:60:p:05 is not listed on IDEAS
    14. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
    15. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    16. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    17. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    18. Michael Goldstein & Michael A. Goldstein & Pavitra Kumar & Frank C. Graves, 2014. "Computerized and High-Frequency Trading," The Financial Review, Eastern Finance Association, vol. 49(2), pages 177-202, May.
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    Cited by:

    1. Henryk Gurgul & Robert Syrek & Christoph Mitterer, 2016. "Price duration versus trading volume in high-frequency data for selected DAX companies," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 17(2), pages 241-260.

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