基于多元驱动因素的主权CDS利差预测研究

Since the outbreak of the European sovereign debt crisis, sovereign risk has attracted increasing attention from industry and academia. Based on the perspective of multiple determinants, this paper adopts several machine learning algorithms to predict sovereign CDS spreads of China and the United States, and combines the mean impact value (MIV) algorithm to analyze the heterogeneity of determinants of sovereign CDS spreads of China and the US. We find that SVR model is in dominant position in the prediction of sovereign CDS spreads of China and the US. Moreover, there are significant differences in driving factors of sovereign CDS spreads of China and the US. Domestic factors (such as S&P 500 index and US dollar index) are relatively important to sovereign CDS spreads of the US, while China’s sovereign CDS spreads is basically determined by global factors. Research conclusions are of great significance to the international financial regulatory coordinating agency to maintain global financial stability, overseas investment of transnational corporation, and international investors to ensure asset security.