IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-88431-3_1.html
   My bibliography  Save this book chapter

Introduction to Bayesian Machine Learning in Quantitative Finance

In: Bayesian Machine Learning in Quantitative Finance

Author

Listed:
  • Wilson Tsakane Mongwe

    (University of Johannesburg)

  • Rendani Mbuvha

    (University of Witwatersrand)

  • Tshilidzi Marwala

    (United Nations University)

Abstract

This chapter introduces the Bayesian framework and how it can be applied to the various areas of quantitative finance, including derivative modeling, banking, financial management, insurance, and investments. We highlight the impact machine learning has had on the finance industry and the potential benefits of framing problems in quantitative finance within the Bayesian framework. The Bayesian framework allows us to naturally answer questions such as: (1) How can we explain the prediction or output of the models? (2) What is the distribution of the parameters of the model? (3) How do we select between the different models in a statistically principled manner? and (4) Which inputs are most relevant for the task at hand? This book aims to answer these questions across various fields within quantitative finance.

Suggested Citation

  • Wilson Tsakane Mongwe & Rendani Mbuvha & Tshilidzi Marwala, 2025. "Introduction to Bayesian Machine Learning in Quantitative Finance," Springer Books, in: Bayesian Machine Learning in Quantitative Finance, chapter 0, pages 1-12, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-88431-3_1
    DOI: 10.1007/978-3-031-88431-3_1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-3-031-88431-3_1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.