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Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise

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Author Info
Yacine Ait-Sahalia
Per A. Mykland
Lan Zhang

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Abstract

We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11380.

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Date of creation: May 2005
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Handle: RePEc:nbr:nberwo:11380

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

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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. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2004. "Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise," OFRC Working Papers Series 2004fe20, Oxford Financial Research Centre. [Downloadable!]
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  2. Yacine Aït-Sahalia, 2005. "How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 18(2), pages 351-416. [Downloadable!] (restricted)
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  3. Ramazan GenÁay & Giuseppe Ballocchi & Michel Dacorogna & Richard Olsen & Olivier Pictet, 2002. "Real-Time Trading Models and the Statistical Properties of Foreign Exchange Rates," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 463-492, May. [Downloadable!] (restricted)
  4. Harris, Lawrence, 1990. " Statistical Properties of the Roll Serial Covariance Bid/Ask Spread Estimator," Journal of Finance, American Finance Association, vol. 45(2), pages 579-90, June. [Downloadable!] (restricted)
  5. Yacine Ait--Sahalia & Per A. Mykland, 2003. "The Effects of Random and Discrete Sampling when Estimating Continuous--Time Diffusions," Econometrica, Econometric Society, vol. 71(2), pages 483-549, March. [Downloadable!] (restricted)
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  6. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
  7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-. [Downloadable!]
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  8. Michael J. Barclay & Terrence Hendershott & D. Timothy McCormick, 2003. "Competition among Trading Venues: Information and Trading on Electronic Communications Networks," Journal of Finance, American Finance Association, vol. 58(6), pages 2637-2666, December. [Downloadable!] (restricted)
  9. 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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  10. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September. [Downloadable!] (restricted)
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Cited by:
(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, 2006. "Estimating Quadratic VariationConsistently in thePresence of Correlated MeasurementError," STICERD - Econometrics Paper Series /2006/509, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
  2. Peter C.B. Phillips & Jun Yu, 2007. "Information Loss in Volatility Measurement with Flat Price Trading," Cowles Foundation Discussion Papers 1598, Cowles Foundation, Yale University. [Downloadable!]
    Other versions:
  3. Neil Shephard & Ole E. Barndorff-Nielsen & Asger Lunde, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics. [Downloadable!]
    Other versions:
  4. Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre. [Downloadable!]
    Other versions:
  5. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 89-104, March. [Downloadable!] (restricted)
  6. Peter C. B. Phillips & Jun Yu, 2005. "Comment on “Realized Variance and Market Microstructure Noise” by Peter R. Hansen and Asger Lunde," Working Papers 13-2005, Singapore Management University, School of Economics. [Downloadable!]
  7. Valentina Corradi & Norman Swanson & Walter Distaso, 2006. "Predictive Density Estimators for Daily Volatility Based on the Use of Realized Measures," Departmental Working Papers 200620, Rutgers University, Department of Economics. [Downloadable!]
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