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Random discretization of stationary continuous time processes

Author

Listed:
  • Anne Philippe

    (Université de Nantes)

  • Caroline Robet

    (Université de Nantes)

  • Marie-Claude Viano

    (Université de Lille 1)

Abstract

This paper investigates second order properties of a stationary continuous time process after random sampling. While a short memory process always gives rise to a short memory one, we prove that long-memory can disappear when the sampling law has very heavy tails. Despite the fact that the normality of the process is not maintained by random sampling, the normalized partial sum process converges to the fractional Brownian motion, at least when the long memory parameter is preserved.

Suggested Citation

  • Anne Philippe & Caroline Robet & Marie-Claude Viano, 2021. "Random discretization of stationary continuous time processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(3), pages 375-400, April.
    • Handle: RePEc:spr:metrik:v:84:y:2021:i:3:d:10.1007_s00184-020-00783-1
    DOI: 10.1007/s00184-020-00783-1
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    References listed on IDEAS

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    1. Henghsiu Tsai & K. S. Chan, 2005. "Quasi‐Maximum Likelihood Estimation for a Class of Continuous‐time Long‐memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 691-713, September.
    2. 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.
    3. F. Comte, 1996. "Simulation And Estimation Of Long Memory Continuous Time Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(1), pages 19-36, January.
    4. Fabienne Comte & Eric Renault, 1998. "Long memory in continuous‐time stochastic volatility models," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 291-323, October.
    5. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, July.
    6. Comte, F. & Renault, E., 1996. "Long memory continuous time models," Journal of Econometrics, Elsevier, vol. 73(1), pages 101-149, July.
    7. Darrell Duffie & Peter Glynn, 2004. "Estimation of Continuous-Time Markov Processes Sampled at Random Time Intervals," Econometrica, Econometric Society, vol. 72(6), pages 1773-1808, November.
    8. Viano, M. C. & Deniau, C. & Oppenheim, G., 1994. "Continuous-time fractional ARMA processes," Statistics & Probability Letters, Elsevier, vol. 21(4), pages 323-336, November.
    9. Chambers, Marcus J., 1996. "The Estimation of Continuous Parameter Long-Memory Time Series Models," Econometric Theory, Cambridge University Press, vol. 12(2), pages 374-390, June.
    10. Henghsiu Tsai & K. S. Chan, 2005. "Maximum likelihood estimation of linear continuous time long memory processes with discrete time data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 703-716, November.
    11. Milton Friedman, 1962. "Introduction to "The Interpolation of Time Series by Related Series"," NBER Chapters, in: The Interpolation of Time Series by Related Series, pages 1-3, National Bureau of Economic Research, Inc.
    12. Lii, Keh-Shin & Masry, Elias, 1994. "Spectral estimation of continuous-time stationary processes from random sampling," Stochastic Processes and their Applications, Elsevier, vol. 52(1), pages 39-64, August.
    13. Shi, Xiaoping & Wu, Yuehua & Liu, Yu, 2010. "A note on asymptotic approximations of inverse moments of nonnegative random variables," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1260-1264, August.
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