Nonparametric machine learning models for predicting the credit default swaps: An empirical study

• Nonparametric machine learning models were compared to predicting the credit default swaps
• Empirical study over a decade including the global financial crisis period were preformed.
• Bayesian neural networks and Gaussian process regression deliver better predictive performances.

Credit default swap which reflects the credit risk of a firm is one of the most frequently traded credit derivatives. In this paper, we conduct a comprehensive study to verify the predictive performance of nonparametric machine learning models and two conventional parametric models on the daily credit default swap spreads of different maturities and different rating groups, from AA to C. The whole period of data set used in this study runs from January 2001 to February 2014, which includes the global financial crisis period when the credit risk of firms were very high. Through experiments, it is shown that most nonparametric models used in this study outperformed the parametric benchmark models in terms of prediction accuracy as well as the practical hedging measures irrespective of the different credit ratings of the firms and the different maturities of their spreads. Especially, artificial neural networks showed better performance than the other parametric and nonparametric models.