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Price discovery in a continuous-time setting

Author

Listed:
  • Gustavo Fruet Dias

    (University of East Anglia)

  • Marcelo Fernandes

    (Sao Paulo School of Economics)

  • Cristina Mabel Scherrer

    (University of East Anglia)

Abstract
We formulate a continuous-time price discovery model and investigate how the standard price discovery measures vary with respect to the sampling interval. We ï¬ nd that the component share measure is invariant to the sampling interval, and hence, discrete-sampled prices suffice to identify the continuous-time component share. In contrast, information share estimates are not comparable across different sampling intervals because the contemporaneous correlation between markets increases in magnitude as the sampling interval grows. We show how to back out the continuous-time information share from discrete-sampled prices under cer-tain assumptions on the contemporaneous correlation. We assess our continuous-time model by comparing the estimates of the (continuous-time) component and information shares at different sampling intervals for 30 stocks in the US. We ï¬ nd that both price discovery measures are typ-ically stable across the different sampling intervals, suggesting that our continuous-time price discovery model ï¬ ts the data very well.

Suggested Citation

  • Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..
    • Handle: RePEc:uea:ueaeco:2019_02
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    References listed on IDEAS

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    1. repec:hal:journl:peer-00815564 is not listed on IDEAS
    2. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2019. "Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 419-435, July.
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    4. Marcelo Fernandes & Cristina M. Scherrer, 2018. "Price discovery in dual‐class shares across multiple markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 129-155, January.
    5. de Jong, Frank, 2002. "Measures of contributions to price discovery: a comparison," Journal of Financial Markets, Elsevier, vol. 5(3), pages 323-327, July.
    6. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
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    13. Baillie, Richard T. & Geoffrey Booth, G. & Tse, Yiuman & Zabotina, Tatyana, 2002. "Price discovery and common factor models," Journal of Financial Markets, Elsevier, vol. 5(3), pages 309-321, July.
    14. Gustavo Fruet Dias & Cristina M. Scherrer & Fotis Papailias, 2016. "Volatility Discovery," CREATES Research Papers 2016-07, Department of Economics and Business Economics, Aarhus University.
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    Cited by:

    1. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    2. 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.
    3. Kuck, Konstantin & Schweikert, Karsten, 2023. "Price discovery in equity markets: A state-dependent analysis of spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 149(C).
    4. Dimpfl, Thomas & Schweikert, Karsten, 2023. "Information shares for markets with partially overlapping trading hours," Journal of Banking & Finance, Elsevier, vol. 154(C).
    5. Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

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    More about this item

    Keywords

    high-frequency data; price discovery; continuous-time model; sampling interval;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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