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A note on calculating autocovariances of long‐memory processes

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  • STEFANO BERTELLI
  • MASSIMILIANO CAPORIN

Abstract

In this paper, we consider a method (splitting) for calculating the auto‐ covariances of fractional integrated processes (ARFIMA) and generalized integrated processes (GARMA). The splitting method does not require any restriction on the autoregressive roots, and allows fast calculation of the autocovariances of these processes.

Suggested Citation

  • Stefano Bertelli & Massimiliano Caporin, 2002. "A note on calculating autocovariances of long‐memory processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(5), pages 503-508, September.
    • Handle: RePEc:bla:jtsera:v:23:y:2002:i:5:p:503-508
    DOI: 10.1111/1467-9892.00275
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    Cited by:

    1. Rossi, Eduardo & Santucci de Magistris, Paolo, 2013. "Long memory and tail dependence in trading volume and volatility," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 94-112.
    2. Hurvich, Cliiford & Wang, Yi, 2006. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 1413, University Library of Munich, Germany.
    3. Kristoufek, Ladislav, 2015. "On the interplay between short and long term memory in the power-law cross-correlations setting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 218-222.
    4. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    5. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    6. Rohit Deo & Mengchen Hsieh & Clifford Hurvich, 2005. "Tracing the Source of Long Memory in Volatility," Econometrics 0501005, University Library of Munich, Germany.

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