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Long range dependence for stable random processes

Author

Listed:
  • Vitalii Makogin
  • Marco Oesting
  • Albert Rapp
  • Evgeny Spodarev

Abstract

We investigate long and short memory in α‐stable moving averages and max‐stable processes with α‐Fréchet marginal distributions. As these processes are heavy‐tailed, we rely on the notion of long range dependence based on the covariance of indicators of excursion sets. Sufficient conditions for the long and short range dependence of α‐stable moving averages are proven in terms of integrability of the corresponding kernel functions. For max‐stable processes, the extremal coefficient function is used to state a necessary and sufficient condition for long range dependence.

Suggested Citation

  • Vitalii Makogin & Marco Oesting & Albert Rapp & Evgeny Spodarev, 2021. "Long range dependence for stable random processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 161-185, March.
  • Handle: RePEc:bla:jtsera:v:42:y:2021:i:2:p:161-185
    DOI: 10.1111/jtsa.12560
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    References listed on IDEAS

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    1. Martin Schlather, 2003. "A dependence measure for multivariate and spatial extreme values: Properties and inference," Biometrika, Biometrika Trust, vol. 90(1), pages 139-156, March.
    2. Beran, Jan & Schell, Dieter, 2012. "On robust tail index estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3430-3443.
    3. Paulauskas, V. J., 1976. "Some remarks on multivariate stable distributions," Journal of Multivariate Analysis, Elsevier, vol. 6(3), pages 356-368, September.
    4. Kokoszka, P. & Mikosch, T., 1997. "The integrated periodogram for long-memory processes with finite or infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 66(1), pages 55-78, February.
    5. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    6. Hira L. Koul & Donatas Surgailis, 2018. "Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(3), pages 273-298, May.
    7. Damarackas, Julius & Paulauskas, Vygantas, 2017. "Spectral covariance and limit theorems for random fields with infinite variance," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 156-175.
    8. Jan Beran & Bikramjit Das & Dieter Schell, 2012. "On robust tail index estimation for linear long‐memory processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(3), pages 406-423, May.
    9. Epaminondas Panas, 2001. "Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 395-402.
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