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The Effects of Random and Discrete Sampling When Estimating Continuous-Time Diffusions

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  • Yacine Ait-Sahalia
  • Per A. Mykland

Abstract

High-frequency financial data are not only discretely sampled in time but the time separating successive observations is often random. We analyze the consequences of this dual feature of the data when estimating a continuous-time model. In particular, we measure the additional effects of the randomness of the sampling intervals over and beyond those due to the discreteness of the data. We also examine the effect of simply ignoring the sampling randomness. We find that in many situations the randomness of the sampling has a larger impact than the discreteness of the data.

Suggested Citation

  • Yacine Ait-Sahalia & Per A. Mykland, 2002. "The Effects of Random and Discrete Sampling When Estimating Continuous-Time Diffusions," NBER Technical Working Papers 0276, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0276
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    References listed on IDEAS

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    Cited by:

    1. repec:kap:compec:v:51:y:2018:i:2:d:10.1007_s10614-017-9692-6 is not listed on IDEAS
    2. Carrasco, Marine & Chernov, Mikhaël & Florens, Jean-Pierre & Ghysels, Eric, 2000. "Efficient Estimation of Jump Diffusions and General Dynamic Models with a Continuum of Moment Conditions," IDEI Working Papers 116, Institut d'Économie Industrielle (IDEI), Toulouse, revised 2002.
    3. George Hall and John Rust, Yale University, 2001. "Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market," Computing in Economics and Finance 2001 274, Society for Computational Economics.
    4. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.
    5. Chorowski, Jakub & Trabs, Mathias, 2016. "Spectral estimation for diffusions with random sampling times," Stochastic Processes and their Applications, Elsevier, vol. 126(10), pages 2976-3008.
    6. Aït-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2011. "Ultra high frequency volatility estimation with dependent microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 160-175, January.
    7. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
    8. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.
    9. Alvaro Cartea & Thilo Meyer-Brandis, 2007. "How Does Duration Between Trades of Underlying Securities Affect Option Prices," Birkbeck Working Papers in Economics and Finance 0721, Birkbeck, Department of Economics, Mathematics & Statistics.
    10. Yacine Aït-Sahalia, 2005. "How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise," Review of Financial Studies, Society for Financial Studies, vol. 18(2), pages 351-416.
    11. Müller, Hans-Georg & Sen, Rituparna & Stadtmüller, Ulrich, 2011. "Functional data analysis for volatility," Journal of Econometrics, Elsevier, vol. 165(2), pages 233-245.
    12. Jianqing Fan & Yingying Fan & Jinchi Lv, 0. "Aggregation of Nonparametric Estimators for Volatility Matrix," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(3), pages 321-357.
    13. Aït-Sahalia, Yacine & Kimmel, Robert L., 2010. "Estimating affine multifactor term structure models using closed-form likelihood expansions," Journal of Financial Economics, Elsevier, vol. 98(1), pages 113-144, October.
    14. Wayne E. Ferson & Andrea Heuson & Tie Su, 2004. "Weak and Semi-Strong Form Stock Return Predictability, Revisited," NBER Working Papers 10689, National Bureau of Economic Research, Inc.
    15. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    16. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    17. repec:sbe:breart:v:24:y:2004:i:2:a:2713 is not listed on IDEAS
    18. Yacine Ait-Sahalia, 2002. "Closed-Form Likelihood Expansions for Multivariate Diffusions," NBER Working Papers 8956, National Bureau of Economic Research, Inc.
    19. Li, Chenxu & Chen, Dachuan, 2016. "Estimating jump–diffusions using closed-form likelihood expansions," Journal of Econometrics, Elsevier, vol. 195(1), pages 51-70.
    20. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
    21. Lars Josef Hook & Erik Lindstrom, 2015. "Efficient Computation of the Quasi Likelihood function for Discretely Observed Diffusion Processes," Papers 1509.07751, arXiv.org.
    22. Cysne, Rubens Penha, 2004. "On the Statistical Estimation of Diffusion Processes: A Partial Survey," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 24(2), November.
    23. Höök, Lars Josef & Lindström, Erik, 2016. "Efficient computation of the quasi likelihood function for discretely observed diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 426-437.
    24. Yacine Ait-Sahalia & Robert Kimmel, 2004. "Maximum Likelihood Estimation of Stochastic Volatility Models," NBER Working Papers 10579, National Bureau of Economic Research, Inc.
    25. Yacine Ait-Sahalia, 2003. "Disentangling Volatility from Jumps," NBER Working Papers 9915, National Bureau of Economic Research, Inc.

    More about this item

    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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