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Testing and Modelling Market Microstructure Effects with an Application to the Dow Jones Industrial Average

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  • Basel Awartani
  • Valentina Corradi

Abstract

It is a well accepted fact that stock returns data are often characterized by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model free measures of volatility, such as realized volatility. In this paper we suggest two test statistics. The first is used to test the null hypothesis of no microstructure noise. If the null is rejected, we proceed to perform a test for the hypothesis that the microstructure noise variance is independent of the sampling frequency at which the data are recorded. We provide empirical evidence based on the Dow Jones Industrial Average, for the period 1997-2002. Our findings suggest that, while the presence of microstructure induces a severe bias when using high frequency data, such a bias grows less than linearly in the number of intraday observations.

Suggested Citation

  • Basel Awartani & Valentina Corradi, 2004. "Testing and Modelling Market Microstructure Effects with an Application to the Dow Jones Industrial Average," Econometric Society 2004 North American Summer Meetings 487, Econometric Society.
  • Handle: RePEc:ecm:nasm04:487
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    References listed on IDEAS

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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    3. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "A Feasible Central Limit Theory for Realised Volatility Under Leverage," Economics Papers 2004-W03, Economics Group, Nuffield College, University of Oxford.
    4. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    5. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    6. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 1-37.
    7. Meddahi, N., 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche 2001-26, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    8. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Multipower Variation and Stochastic Volatility," OFRC Working Papers Series 2004fe22, Oxford Financial Research Centre.
    9. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2004. "Analytical Evaluation Of Volatility Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(4), pages 1079-1110, November.
    10. Heiko Ebens, 1999. "Realized Stock Volatility," Economics Working Paper Archive 420, The Johns Hopkins University,Department of Economics, revised Jul 1999.
    11. Maureen O'Hara, 2003. "Presidential Address: Liquidity and Price Discovery," Journal of Finance, American Finance Association, vol. 58(4), pages 1335-1354, August.
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    Cited by:

    1. Ilze Kalnina & Oliver Linton, 2007. "Inference about Realized Volatility using Infill Subsampling," STICERD - Econometrics Paper Series 523, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.

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    More about this item

    Keywords

    bipower variation; market microstructure; realized volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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