Forecasting sovereign CDS spreads with a regime-switching combination method

Abstract

With the growing importance of the sovereign credit default swap (CDS) market, accurate forecasting of sovereign CDS spreads has gained significant attention. In view of the complex volatility in the series of sovereign CDS spreads, this study presents a novel combination forecasting framework, which introduces time-varying weights to effectively combine diverse individual models. To identify optimal subsets of models, a mutual information approach is employed, while the regime-switching method is utilized to integrate the selected models. The proposed method's efficacy is validated using data from 65 countries. Empirical findings underscore the superiority of the proposed approach over benchmark models in terms of both horizontal and directional prediction accuracy, particularly when the sovereign CDS data exhibits a balanced distribution between high and low volatility regimes.