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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Financial forecasting,Nonparametric models,Credit default swap,Empirical analysis

论文评审过程：Received 18 December 2014, Revised 28 March 2016, Accepted 29 March 2016, Available online 4 April 2016, Version of Record 26 April 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.049