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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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US and Overseas: +1 646-931-9045
UK: 0207 139 1600