IDEAS home Printed from https://ideas.repec.org/a/ibn/ijspjl/v10y2021i1p58.html
   My bibliography  Save this article

A Bayesian Approach for Large Asset Allocation

Author

Listed:
  • Mihnea S. Andrei
  • John S. J. Hsu

Abstract

The Black-Litterman model combines investor’s personal views with historical data and gives optimal portfolio weights. In (Andrei & Hsu, 2020), they reviewed the original Black-Litterman model and modified it in order to fit it into a Bayesian framework, when a certain number of assets is considered. They used the idea by (Leonard & Hsu, 1992) for a multivariate normal prior on the logarithm of the covariance matrix. When implemented and applied to a large number of assets such as all the S&P500 companies, they ran into memory allocation and running time issues. In this paper, we reduce the dimensions by considering Bayesian factor models, which solve the asset allocation problems for a large number of assets. In addition, we will conduct sensitivity analysis for the confidence levels that the investors have to input.

Suggested Citation

  • Mihnea S. Andrei & John S. J. Hsu, 2021. "A Bayesian Approach for Large Asset Allocation," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(1), pages 1-58, January.
    • Handle: RePEc:ibn:ijspjl:v:10:y:2021:i:1:p:58
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijsp/article/download/0/0/44345/46946
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/44345
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Mihnea S. Andrei & Sujit K. Ghosh & Jian Zou, 2021. "Dynamic Correlation Multivariate Stochastic Volatility Black-Litterman With Latent Factors," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 1-1, March.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:ijspjl:v:10:y:2021:i:1:p: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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.