Forecasting sovereign CDS returns via deep learning - Lima, Pedro Antonio Sá Barreto de (2021)
This paper is an empirical exercise of forecasting for a class of credit derivatives known as sovereign credit default swaps. Utilizing non-linear and non-parametric machine learning techniques termed Deep Learning, this study was made utilizing daily emerging country data and a set of financial and macroeconomic indicators as features. The non-linear nature of the financial derivative studied here suggests that this novel technique can better capture data behavior compared to a baseline random walk model. A Grid Search Cross-validation is conducted to estimate the hyperparameters of the model. To evaluate the predictive out of sample forecasting is utilized deterministic and statistical metrics concluding that there is a predictive gain utilizing this technique.