IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v39y2021i2p520-531.html
   My bibliography  Save this article

A Nodewise Regression Approach to Estimating Large Portfolios

Author

Listed:
  • Laurent Callot
  • Mehmet Caner
  • A. Özlem Önder
  • Esra Ulaşan

Abstract

This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show that the variance, weights, and risk of the global minimum variance portfolios and the Markowitz mean-variance portfolios are consistently estimated with more assets than observations. We show, empirically, that the nodewise regression-based approach performs well in comparison to factor models and shrinkage methods.Supplementary materials for this article are available online.

Suggested Citation

  • Laurent Callot & Mehmet Caner & A. Özlem Önder & Esra Ulaşan, 2021. "A Nodewise Regression Approach to Estimating Large Portfolios," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 520-531, March.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:2:p:520-531
    DOI: 10.1080/07350015.2019.1683018
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2019.1683018
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07350015.2019.1683018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tae-Hwy Lee & Ekaterina Seregina, 2024. "Optimal Portfolio Using Factor Graphical Lasso," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 670-695.
    2. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).
    3. Yilun Wang & Shengjie Guo, 2024. "RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction," Papers 2403.02500, arXiv.org.
    4. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
    5. Sumanjay Dutta & Shashi Jain, 2023. "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation," Papers 2305.11298, arXiv.org.
    6. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
    7. Anik Burman & Sayantan Banerjee, 2021. "High-dimensional Portfolio Optimization using Joint Shrinkage," Papers 2109.13633, arXiv.org.
    8. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.
    9. Esra Ulasan & A. Özlem Önder, 2023. "Large portfolio optimisation approaches," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 485-497, October.
    10. Saman Banafti & Tae-Hwy Lee, 2022. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Working Papers 202203, University of California at Riverside, Department of Economics.
    11. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    12. Mehmet Caner, 2021. "A Starting Note: A Historical Perspective in Lasso," International Econometric Review (IER), Econometric Research Association, vol. 13(1), pages 1-3, March.
    13. Caner, Mehmet & Medeiros, Marcelo & Vasconcelos, Gabriel F.R., 2023. "Sharpe Ratio analysis in high dimensions: Residual-based nodewise regression in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 393-417.
    14. Dutta, Sumanjay & Jain, Shashi, 2024. "Shrinkage and thresholding approaches for expected utility portfolios: An analysis in terms of predictive ability," Finance Research Letters, Elsevier, vol. 64(C).
    15. Mehmet Caner & Xu Han, 2021. "An upper bound for functions of estimators in high dimensions," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 1-13, January.
    16. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    17. Mehmet Caner & Qingliang Fan & Yingying Li, 2024. "Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints," Papers 2402.17523, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlbes:v:39:y:2021:i:2:p:520-531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.