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Deep Learning Credit Risk Modeling

Gerardo Manzo and Xiao Qiao
The Journal of Fixed Income Fall 2021, jfi.2021.1.121; DOI: https://doi.org/10.3905/jfi.2021.1.121
Gerardo Manzo
is a quantitative researcher and portfolio manager at Kepos Capital in New York City, NY
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Xiao Qiao
is an assistant professor at the City University of Hong Kong and a member of the Hong Kong Institute for Data Science in Kowloon Tong, Hong Kong
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Abstract

This article demonstrates how deep learning can be used to price and calibrate models of credit risk. Deep neural networks can learn structural and reduced-form models with high degrees of accuracy. For complex credit risk models with no closed-form solutions available, deep learning offers a conceptually simple and more efficient alternative solution. This article proposes an approach that combines deep learning with the unscented Kalman filter to calibrate credit risk models based on historical data; this strategy attains an in-sample R-squared of 98.5% for the reduced-form model and 95% for the structural model.

TOPICS: Credit risk management, big data/machine learning, quantitative methods, statistical methods

Key Findings

  • ▪ Neural networks can approximate solutions to credit risk models, precisely capturing the relationship between model inputs and credit spreads.

  • ▪ Compared to standard techniques, the approximate solutions are more computationally efficient.

  • ▪ Neural networks can be used to accurately calibrate structural and reduced-form models of credit risk.

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The Journal of Fixed Income: 31 (4)
The Journal of Fixed Income
Vol. 31, Issue 4
Spring 2022
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Deep Learning Credit Risk Modeling
Gerardo Manzo, Xiao Qiao
The Journal of Fixed Income Aug 2021, jfi.2021.1.121; DOI: 10.3905/jfi.2021.1.121

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Deep Learning Credit Risk Modeling
Gerardo Manzo, Xiao Qiao
The Journal of Fixed Income Aug 2021, jfi.2021.1.121; DOI: 10.3905/jfi.2021.1.121
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  • Article
    • Abstract
    • LITERATURE REVIEW AND CONTRIBUTION
    • CREDIT RISK MODELING
    • STRUCTURAL MODELS OF CREDIT RISK
    • DEEP NEURAL NETWORKS
    • USE OF NEURAL NETWORKS TO PREDICT CREDIT SPREADS
    • DEEP LEARNING CALIBRATION
    • CONCLUSION
    • APPENDIX
    • ENDNOTES
    • REFERENCES
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