IDEAS home Printed from https://ideas.repec.org/a/wsi/afexxx/v16y2021i01ns2010495221500056.html
   My bibliography  Save this article

Regularization Methods For Estimating A Multi-Factor Corporate Bond Pricing Model: An Application For Brazil

Author

Listed:
  • PAULO ROBERTO GUIMARÃES

    (Graduate School in Economics, Catholic University of Brasilia, Brazil)

  • OSVALDO CANDIDO

    (Graduate School in Economics, Catholic University of Brasilia, Brazil)

  • ANDRÉ RONZANI

    (Graduate School in Economics, Catholic University of Brasilia, Brazil)

Abstract

The present work focused on studying which factors affect Brazilian inflation-linked corporate bond prices in a primary market setting. The explanatory variables tested were rating, maturity, duration, issuer governance level, industrial classification, collateral, tax exemption, public offering modality, financial volume, coupon frequency, number of issues, number of days since going public, and the Brazilian basic interest rate target. In order to choose the set of variables with best predictive performance, best subsets ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO) were applied on a testing sample. For estimating purposes, we also tested the Ridge estimator. For both LASSO and Ridge, we used the k-fold approach to choose the optimal value for the lambda penalty. In terms of smallest mean squared error, the OLS estimator outperformed both the Ridge and the LASSO. This result suggests that the variance-bias trade-off might not be a concern for the Brazilian case.

Suggested Citation

  • Paulo Roberto Guimarães & Osvaldo Candido & André Ronzani, 2021. "Regularization Methods For Estimating A Multi-Factor Corporate Bond Pricing Model: An Application For Brazil," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-20, March.
    • Handle: RePEc:wsi:afexxx:v:16:y:2021:i:01:n:s2010495221500056
    DOI: 10.1142/S2010495221500056
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S2010495221500056
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S2010495221500056?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. Moawia Alghalith & Wing-Keung Wong, 2022. "Option Pricing Under an Abnormal Economy: using the Square Root of the Brownian Motion," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(Special), pages 4-18, December.

    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:wsi:afexxx:v:16:y:2021:i:01:n:s2010495221500056. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/afe/afe.shtml .

    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.