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Price Discovery in Fragmented Markets

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  • de Jong, Frank
  • Schotman, Peter C

Abstract

This Paper proposes a structural time series model for the intra-day price dynamics of fragmented financial markets. We generalize the structural model of Hasbrouck (1993) to a multivariate setting. We discuss identification issues and propose a new measure for the contribution of each market to price discovery. We illustrate the model by an empirical example using Nasdaq dealer quotes.

Suggested Citation

  • de Jong, Frank & Schotman, Peter C, 2003. "Price Discovery in Fragmented Markets," CEPR Discussion Papers 3987, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:3987
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    8. Hasbrouck, Joel, 1995. "One Security, Many Markets: Determining the Contributions to Price Discovery," Journal of Finance, American Finance Association, vol. 50(4), pages 1175-1199, September.
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    10. Covrig, Vicentiu & Melvin, Michael, 2002. "Asymmetric information and price discovery in the FX market: does Tokyo know more about the yen?," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 271-285, August.
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    Cited by:

    1. Scherrer, Cristina Mabel, 2021. "Information processing on equity prices and exchange rate for cross-listed stocks," Journal of Financial Markets, Elsevier, vol. 54(C).
    2. Gustavo F. Dias & Marcelo Fernandes & Cristina M. Scherrer, 2021. "Price Discovery in a Continuous-Time Setting [Price Discovery and Common Factor Models]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 985-1008.
    3. Karsten Schweikert, 2021. "Bootstrap Confidence Intervals and Hypothesis Testing for Market Information Shares [Price Discovery and Common Factor Models]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 934-959.
    4. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    5. Osama Ahmed, 2021. "Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach," Mathematics, MDPI, vol. 9(2), pages 1-21, January.
    6. Joakim Westerlund & Simon Reese & Paresh Narayan, 2017. "A Factor Analytical Approach to Price Discovery," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(3), pages 366-394, June.
    7. Sebastiano Michele Zema & Francesco Cordoni, 2023. "A non-Normal framework for price discovery: The independent component based information shares measure," LEM Papers Series 2023/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Lepone, Andrew & Yang, Jin Young, 2013. "Informational role of market makers: The case of exchange traded CFDs," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 84-92.
    9. Ozturk, Sait R. & van der Wel, Michel & van Dijk, Dick, 2017. "Intraday price discovery in fragmented markets," Journal of Financial Markets, Elsevier, vol. 32(C), pages 28-48.
    10. Giuliodori, Massimo & Beetsma, Roel & de Jong, Frank & Widijanto, Daniel, 2014. "The impact of news and the SMP on realized (co)variances in the eurozone sovereign debt market," Working Paper Series 1629, European Central Bank.
    11. Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
    12. David Evangelista & Yuri Saporito & Yuri Thamsten, 2022. "Price formation in financial markets: a game-theoretic perspective," Papers 2202.11416, arXiv.org.
    13. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    14. Lukasz Gatarek & Soeren Johansen, 2017. "The role of cointegration for optimal hedging with heteroscedastic error term," Discussion Papers 17-03, University of Copenhagen. Department of Economics.
    15. Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    16. Wang, Jianxin & Yang, Minxian, 2015. "How well does the weighted price contribution measure price discovery?," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 113-129.
    17. Donald Lien & Zijun Wang, 2016. "Estimation of Market Information Shares: A Comparison," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(11), pages 1108-1124, November.
    18. Sait R. Ozturk & Michel van der Wel & Dick van Dijk, 2015. "Why do Pit-Hours outlive the Pit?," Tinbergen Institute Discussion Papers 15-082/III, Tinbergen Institute.
    19. Ahmed, Osama, 2021. "Assessing the current situation of the world wheat market leadership: Using the semi-parametric approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(2).
    20. Sebastiano Michele Zema, 2020. "Directed Acyclic Graph based Information Shares for Price Discovery," LEM Papers Series 2020/28, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    21. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    22. Beetsma, Roel & de Jong, Frank & Giuliodori, Massimo & Widijanto, Daniel, 2017. "Realized (co)variances of eurozone sovereign yields during the crisis: The impact of news and the Securities Markets Programme," Journal of International Money and Finance, Elsevier, vol. 75(C), pages 14-31.
    23. Sobti, Neharika & Sehgal, Sanjay & Ilango, Balakrishnan, 2021. "How do macroeconomic news surprises affect round-the-clock price discovery of gold?," International Review of Financial Analysis, Elsevier, vol. 78(C).

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    More about this item

    Keywords

    High-frequency data; Microstructure; Structural time series models;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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