RT Journal Article
SR Electronic
T1 Deep Learning Credit Risk Modeling
JF The Journal of Fixed Income
FD Institutional Investor Journals
SP 101
OP 127
DO 10.3905/jfi.2021.1.121
VO 31
IS 2
A1 Gerardo Manzo
A1 Xiao Qiao
YR 2021
UL https://pm-research.com/content/31/2/101.abstract
AB 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.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.