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

  • Walter Distaso
  • Basel Awartani
  • Valentina Corradi
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    It is a well accepted fact that stock returns data are often contaminated 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 for 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 data are recorded. We provide empirical evidence based on the stocks included in the Dow Jones Industrial Average, for the period 1997-2002. Our findings suggest that, while the presence of microstructure induces a severe bias when estimating volatility using high frequency data, such a bias grows less than linearly in the number of intraday observatio

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    Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 273.

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    Date of creation: 11 Aug 2004
    Date of revision:
    Handle: RePEc:ecm:ausm04:273
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    1. Neil Shephard & Ole Barndorff-Nielsen, 2003. "A feasible central limit theory for realised volatility under leverage," Economics Series Working Papers 2004-FE-03, University of Oxford, Department of Economics.
    2. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2002. "Analytic Evaluation of Volatility Forecasts," CIRANO Working Papers 2002s-90, CIRANO.
    3. Ole E. Barndorff-Nielsen & 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.
    4. 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.
    5. Heiko Ebens, 1999. "Realized Stock Volatility," Economics Working Paper Archive 420, The Johns Hopkins University,Department of Economics, revised Jul 1999.
    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. 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.
    8. Ole Barndorff-Nielsen & Neil Shephard, 2004. "Multipower Variation and Stochastic Volatility," Economics Papers 2004-W30, Economics Group, Nuffield College, University of Oxford.
    9. 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.
    10. Maureen O'Hara, 2003. "Presidential Address: Liquidity and Price Discovery," Journal of Finance, American Finance Association, vol. 58(4), pages 1335-1354, 08.
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