IDEAS home Printed from https://ideas.repec.org/h/elg/eechap/20452_7.html
   My bibliography  Save this book chapter

Market-liquidity risk modeling and reinforcement machine learning algorithms under extreme market outlooks: applications to emerging markets

In: Handbook of Banking and Finance in Emerging Markets

Author

Listed:
  • Mazin A.M. Al Janabi

Abstract

This chapter examines the different aspects for the development of robust risk modeling techniques that attempt to address the problem of market-liquidity risk of multi-assets portfolios under extreme market outlooks, using reinforcement machine learning algorithms. The modeling algorithms and optimization techniques discussed in this chapter can aid in advancing risk and portfolio management practices in emerging markets, particularly in wake of the 2007-2009 financial crisis. Furthermore, the proposed risk management modeling techniques and optimization algorithms can have key applications in reinforcement machine learning, expert systems, smart financial functions, Internet of Things (IoT), and financial technology (FinTech) in big data ecosystems.

Suggested Citation

  • Mazin A.M. Al Janabi, 2022. "Market-liquidity risk modeling and reinforcement machine learning algorithms under extreme market outlooks: applications to emerging markets," Chapters, in: Duc K. Nguyen (ed.), Handbook of Banking and Finance in Emerging Markets, chapter 7, pages 115-130, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:20452_7
    as

    Download full text from publisher

    File URL: https://www.elgaronline.com/view/edcoll/9781800880900/9781800880900.00013.xml
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Development Studies; Economics and Finance;

    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:elg:eechap:20452_7. 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: Darrel McCalla (email available below). General contact details of provider: http://www.e-elgar.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.