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Component shares in continuous time

Author

Listed:
  • Gustavo Fruet Dias

    (Aarhus University and CREATES)

  • Marcelo Fernandes

    (School of Economics and Finance)

  • Cristina M. Scherrer

    (Aarhus University and CREATES)

Abstract

We formulate a continuous-time price discovery model and investigate how the standard price discovery measures vary with respect to the sampling frequency. We find that the component share measure is invariant to the sampling frequency, and hence a continuous-time price discovery measure can be identified from discrete sampled prices. We also contribute by proposing a novel estimation strategy for the continuous-time component share. We establish consistency and asymptotic normality of a kernel-based estimator that compares favourably to the standard daily VECM regression. Finally, we compute daily estimates of price discovery for 30 stocks in the U.S. from 2007 to 2013.

Suggested Citation

  • Gustavo Fruet Dias & Marcelo Fernandes & Cristina M. Scherrer, 2016. "Component shares in continuous time," CREATES Research Papers 2016-25, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2016-25
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    high-frequency data; price discovery; continuous-time model; sampling frequency; time-varying coefficients;
    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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