IMF Working Papers

Completing the Market: Generating Shadow CDS Spreads by Machine Learning

By Nan Hu, Jian Li, Alexis Meyer-Cirkel

December 27, 2019

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Format: Chicago

Nan Hu, Jian Li, and Alexis Meyer-Cirkel. Completing the Market: Generating Shadow CDS Spreads by Machine Learning, (USA: International Monetary Fund, 2019) accessed November 12, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.

Subject: Credit default swap, Credit ratings, Credit risk, Financial markets, Financial regulation and supervision, Machine learning, Money, Stock markets, Technology

Keywords: Contracts data, Coverage ratio, Credit default swap, Credit default swaps, Credit measure Distance to default, Credit ratings, Credit risk, Default probability, Equity market variable, Failure intensity, Firm, Firm size proxy, Importance probability matrix, Input variable, Machine learning, Machine learning method, Machine Learning methods, Macroeconomic variable, Market perception, North America, Prediction, Recovery rate, Stock markets, WP

Publication Details

  • Pages:

    37

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2019/292

  • Stock No:

    WPIEA2019292

  • ISBN:

    9781513524085

  • ISSN:

    1018-5941

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