The Determinants of Credit Default Swap Premia and the Use of Machine Learning Techniques for their Estimation

Feser, Julian Alexander and Broby, Daniel (2020) The Determinants of Credit Default Swap Premia and the Use of Machine Learning Techniques for their Estimation. Discussion paper. University of Strathclyde, Glasgow.

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Abstract

This paper examines the determinants of Credit Default Swap premia. It also explores the use of Machine Learning techniques for their estimation. We address default risk, counter-party risk and liquidity risk. We discuss these in the context of yield curves, maturity and volatility. The insights gained are used to illustrate how the use of technology can provide more timely, efficient and informative valuations. We recommend the use of support vector and artificial neural networks (supervised learning models), as well as principal component analysis. Combined with standardized electronic processing and central clearing of trade, we suggest that this will enhance the depth of CDS markets. At the same time, Machine Learning can also aid the understanding of the various premia. We conclude that the application of Artificial Intelligence can add significant economic value to banking operations.

  • Item type:Monograph(Discussion paper)
    ID code:73801
    Dates:
    Date
    Event
    3 September 2020
    Published
    Notes:Financial technology paper, published as part of the Centre for Financial Regulation and Innovation, Strathclyde Business School, University of Strathclyde.
    Subjects:Social Sciences > Finance
    Department:Strathclyde Business School > Accounting and Finance
    Depositing user: Pure Administrator
    Date deposited:15 Sep 2020 08:10
    Last modified:09 Apr 2024 04:44
    URI:https://strathprints.strath.ac.uk/id/eprint/73801
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