Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals
提出一种基于回归树的新方法，通过预设指标阈值预警主权债务危机，在拟合和预测之间取得最佳平衡，发现流动性不足、违约历史、GDP增长和美国利率是主要决定因素。
Abstract In this article, we try to realize the best compromise between in‐sample goodness of fit and out‐of‐sample predictability of sovereign defaults. To do this, we use a new regression‐tree based approach that signals impending sovereign debt crises whenever pre‐selected indicators exceed specific thresholds. Using data from emerging markets and Greece, Ireland, Portugal and Spain (GIPS) over the period 1975–2010, we show that our model significantly outperforms existing competing approaches (logit, stepwise logit, noise‐to‐signal ratio and regression trees), while balancing in‐ and out‐of‐sample performance. Our results indicate that illiquidity (high short‐term debt to reserves) and default history, together with real GDP growth and US interest rates, are the main determinants of both emerging market country defaults and the recent European sovereign debt crisis.