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Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals
Author(s) -
Savona Roberto,
Vezzoli Marika
Publication year - 2015
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12052
Subject(s) - default , predictability , econometrics , economics , emerging markets , sample (material) , credit risk , debt , financial economics , logit , actuarial science , statistics , macroeconomics , finance , mathematics , chemistry , chromatography
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.