Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals

type="main" xml:id="obes12052-abs-0001"> 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.