Forecasting and trading credit default swap indices using a deep learning model integrating Merton and LSTMs

作者：

Highlights：

• Integrating financial conditions with LSTM improves CDS forecasting performance.

• Merton-LSTM outperforms GRU, SVM, MLP and stochastic series model in forecasting CDS.

• The Merton-LSTM trading strategy eclipses other tested trading strategies.

• The superiority of the Merton-LSTM model proved in long-term prediction.

摘要

•Integrating financial conditions with LSTM improves CDS forecasting performance.•Merton-LSTM outperforms GRU, SVM, MLP and stochastic series model in forecasting CDS.•The Merton-LSTM trading strategy eclipses other tested trading strategies.•The superiority of the Merton-LSTM model proved in long-term prediction.

论文关键词：Forecasting,Trading,Credit default swap,LSTM,Deep learning

论文评审过程：Received 14 March 2022, Revised 30 August 2022, Accepted 9 October 2022, Available online 13 October 2022, Version of Record 20 October 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.119012