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We modeled long memory with just one lag!

Author

Listed:
  • Bauwens, Luc

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Chevillon, Guillaume

    (ESSEC Business School)

  • Laurent, Sébastien

    (Aix-Marseille University)

Abstract

Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.

Suggested Citation

  • Bauwens, Luc & Chevillon, Guillaume & Laurent, Sébastien, 2023. "We modeled long memory with just one lag!," LIDAM Reprints CORE 3234, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:3234
    DOI: https://doi.org/10.1016/j.jeconom.2023.04.010
    Note: In: Journal of Econometrics, 2023, vol. 236(1), 105467
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    Cited by:

    1. Shikta Sing & Supun Chandrasena & Yue Shi & Abdullah Alhussain & Claude Diebolt & Martin Enilov & Tapas Mishra, 2024. "A Learning Model with Memory in the Financial Markets," Working Papers of BETA 2024-41, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Jie Wang & Yongqiao Wang, 2025. "Forecasting Expected Shortfall and Value‐at‐Risk With Cross‐Sectional Aggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 391-423, March.
    3. Anna Mikusheva & Mikkel Sølvsten, 2025. "Linear regression with weak exogeneity," Quantitative Economics, Econometric Society, vol. 16(2), pages 367-403, May.
    4. Castro, Tomas del Barrio & Escribano, Alvaro & Sibbertsen, Philipp, 2025. "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data," Energy Economics, Elsevier, vol. 147(C).

    More about this item

    Keywords

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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