IDEAS home Printed from https://ideas.repec.org/a/taf/tstfxx/v1y2017i1p48-58.html
   My bibliography  Save this article

Cholesky-based model averaging for covariance matrix estimation

Author

Listed:
  • Hao Zheng
  • Kam-Wah Tsui
  • Xiaoning Kang
  • Xinwei Deng

Abstract

Estimation of large covariance matrices is of great importance in multivariate analysis. The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables. However, information on the order of variables is often unknown, or cannot be reasonably assumed in practice. In this work, we propose a Cholesky-based model averaging approach of covariance matrix estimation for high dimensional data with proper regularisation imposed on the Cholesky factor matrix. The proposed method not only guarantees the positive definiteness of the covariance matrix estimate, but also is applicable in general situations without the order of variables being pre-specified. Numerical simulations are conducted to evaluate the performance of the proposed method in comparison with several other covariance matrix estimates. The advantage of our proposed method is further illustrated by a real case study of equity portfolio allocation.

Suggested Citation

  • Hao Zheng & Kam-Wah Tsui & Xiaoning Kang & Xinwei Deng, 2017. "Cholesky-based model averaging for covariance matrix estimation," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 1(1), pages 48-58, January.
  • Handle: RePEc:taf:tstfxx:v:1:y:2017:i:1:p:48-58
    DOI: 10.1080/24754269.2017.1336831
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24754269.2017.1336831
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/24754269.2017.1336831?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. Gao, Zhenguo & Wang, Xinye & Kang, Xiaoning, 2023. "Ensemble LDA via the modified Cholesky decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    2. Kang, Xiaoning & Wang, Mingqiu, 2021. "Ensemble sparse estimation of covariance structure for exploring genetic disease data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    3. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    4. Liang, Wanfeng & Ma, Xiaoyan, 2024. "A new approach for ultrahigh-dimensional covariance matrix estimation," Statistics & Probability Letters, Elsevier, vol. 204(C).
    5. Yuan, Chaoxia & Fang, Fang & Ni, Lyu, 2022. "Mallows model averaging with effective model size in fragmentary data prediction," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    6. Fang, Fang & Yang, Qiwei & Tian, Wenling, 2022. "Cross-validation for selecting the penalty factor in least squares model averaging," Economics Letters, Elsevier, vol. 217(C).

    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:tstfxx:v:1:y:2017:i:1:p:48-58. 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/tstf .

    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.