Forecasting China's sovereign CDS with a decomposition reconstruction strategy

Precisely predicting sovereign credit default swaps (CDS) is important for preventing sovereign risks. However, sovereign CDS exhibit complex nonlinear characteristics, which make accurate predictions challenging. As a result, this paper proposes a hybrid ensemble forecasting framework based on a decomposition reconstruction strategy that considers multiple factors. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the sovereign CDS into different components, and the reconstruction method based on sample entropy is employed to reconstruct the components into trend items, market fluctuation items and noise items. Motivated by the features of components reconstructed, we utilize the autoregressive integrated moving average (ARIMA) and relevance vector machine (RVM) models to predict each component. Finally, the forecasting results of the three sub-sequences are integrated as the final forecast. For verification, China's 5-year daily sovereign CDS is used as sample data, and the empirical results reveal that the proposed model performs better than all benchmark models in terms of forecasting accuracy, including horizon and directional. Furthermore, the results indicate that the proposed model can be regarded as an effective and promising tool for forecasting China's sovereign CDS. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel hybrid ensemble forecasting framework for predicting sovereign CDS using CEEMDAN decomposition and reconstruction strategy. Empirical results demonstrate that the model outperforms benchmark models in terms of forecasting accuracy, indicating its potential as an effective forecasting tool.