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Price Discovery in Fragmented Markets

Author

Listed:
  • 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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    References listed on IDEAS

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    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Lehmann, Bruce N., 2002. "Some desiderata for the measurement of price discovery across markets," Journal of Financial Markets, Elsevier, vol. 5(3), pages 259-276, July.
    3. 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.
    4. Upper, Christian & Werner, Thomas, 2002. "Tail Wags Dog? Time-Varying Information Shares in the Bund Market," Discussion Paper Series 1: Economic Studies 2002,24, Deutsche Bundesbank.
    5. Roger D. Huang, 2002. "The Quality of ECN and Nasdaq Market Maker Quotes," Journal of Finance, American Finance Association, vol. 57(3), pages 1285-1319, June.
    6. Hupperets, Erik C. J. & Menkveld, Albert J., 2002. "Intraday analysis of market integration: Dutch blue chips traded in Amsterdam and New York," Journal of Financial Markets, Elsevier, vol. 5(1), pages 57-82, January.
    7. Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
    8. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    9. deB. Harris, Frederick H. & McInish, Thomas H. & Wood, Robert A., 2002. "Security price adjustment across exchanges: an investigation of common factor components for Dow stocks," Journal of Financial Markets, Elsevier, vol. 5(3), pages 277-308, July.
    10. de Jong, Frank & Mahieu, Ronald J & Schotman, Peter C, 1999. "Price Discovery on Foreign Exchange Markets," CEPR Discussion Papers 2296, C.E.P.R. Discussion Papers.
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    Citations

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    Cited by:

    1. repec:bla:obuest:v:79:y:2017:i:3:p:366-394 is not listed on IDEAS
    2. 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.
    3. 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.
    4. 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.
    5. Lukasz Gatarek & Søren Johansen, 0703. "The role of cointegration for optimal hedging with heteroscedastic error term," CREATES Research Papers 2017-12, Department of Economics and Business Economics, Aarhus University.
    6. 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.
    7. 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.
    8. repec:eee:jimfin:v:75:y:2017:i:c:p:14-31 is not listed on IDEAS

    More about this item

    Keywords

    high-frequency data; microstructure; structural time series models;

    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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