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

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Author Info
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.

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Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 487.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:487

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Keywords: bipower variation market microstructure realized volatility

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  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. [Downloadable!] (restricted)
  2. 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. [Downloadable!] (restricted)
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  3. 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. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
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  5. 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. [Downloadable!]
  6. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37. [Downloadable!] (restricted)
  7. Heiko Ebens, 1999. "Realized Stock Volatility," Economics Working Paper Archive 420, The Johns Hopkins University,Department of Economics, revised Jul 1999.
  8. Maureen O'Hara, 2003. "Presidential Address: Liquidity and Price Discovery," Journal of Finance, American Finance Association, vol. 58(4), pages 1335-1354, 08. [Downloadable!] (restricted)
  9. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Econometrics of testing for jumps in financial economics using bipower variation ," OFRC Working Papers Series 2004fe01, Oxford Financial Research Centre. [Downloadable!]
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Ilze Kalnina & Oliver Linton, 2007. "Inference about Realized Volatility using Infill Subsampling," STICERD - Econometrics Paper Series /2007/523, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
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