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A simple microstructure return model explaining microstructure noise and Epps effects

Author

Listed:
  • A. Saichev

    (Department of Management, ETH Zurich Technology and Economics, Scheuchzerstrasse 7, CH-8092 Zurich, Switzerland;
    Department of Mathematics, Nizhni Novgorod State University, Russia)

  • D. Sornette

    (Department of Management, ETH Zurich Technology and Economics, Scheuchzerstrasse 7, CH-8092 Zurich, Switzerland;
    Swiss Finance Institute, 40, Boulevard du Pont-d' Arve, Case Postale 3, 1211 Geneva 4, Switzerland)

Abstract

We present a novel simple microstructure model of financial returns that combines (i) the well-known ARFIMA process applied to tick-by-tick returns, (ii) the bid-ask bounce effect, (iii) the fat tail structure of the distribution of returns and (iv) the non-Poissonian statistics of inter-trade intervals. This model allows us to explain both qualitatively and quantitatively important stylized facts observed in the statistics of both microstructure and macrostructure returns, including the short-ranged correlation of returns, the long-ranged correlations of absolute returns, the microstructure noise and Epps effects. According to the microstructure noise effect, volatility is a decreasing function of the time-scale used to estimate it. The Epps effect states that cross correlations between asset returns are increasing functions of the time-scale at which the returns are estimated. The microstructure noise is explained as the result of the negative return correlations inherent in the definition of the bid-ask bounce component (ii). In the presence of a genuine correlation between the returns of two assets, the Epps effect is due to an average statistical overlap of the momentum of the returns of the two assets defined over a finite time-scale in the presence of the long memory process (i).

Suggested Citation

  • A. Saichev & D. Sornette, 2014. "A simple microstructure return model explaining microstructure noise and Epps effects," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(06), pages 1-36.
  • Handle: RePEc:wsi:ijmpcx:v:25:y:2014:i:06:n:s0129183114500120
    DOI: 10.1142/S0129183114500120
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    References listed on IDEAS

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    1. Ghysels, E. & Gourieroux, C. & Jasiak, J., 1995. "Market Time and Asset Price Movements: Theory and Estimation," Cahiers de recherche 9536, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-061, New York University, Leonard N. Stern School of Business-.
    5. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
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    Cited by:

    1. Patrick Chang & Roger Bukuru & Tim Gebbie, 2019. "Revisiting the Epps effect using volume time averaging: An exercise in R," Papers 1912.02416, arXiv.org, revised Feb 2020.
    2. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms," Papers 2003.02842, arXiv.org, revised Nov 2020.
    3. Filimonov, Vladimir & Sornette, Didier, 2015. "Power law scaling and “Dragon-Kings” in distributions of intraday financial drawdowns," Chaos, Solitons & Fractals, Elsevier, vol. 74(C), pages 27-45.
    4. Vladimir Filimonov & Didier Sornette, 2014. "Power law scaling and "Dragon-Kings" in distributions of intraday financial drawdowns," Papers 1407.5037, arXiv.org, revised Apr 2015.

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    More about this item

    Keywords

    High-frequency trading; micro-structure; Epps effect; long memory; momentum; JEL Classification: C32; JEL Classification: G17;
    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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