Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques
Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques
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DOI：
10.3390/sym13040652
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发表时间：
2021-04-01
期刊：
影响因子：
2.7
通讯作者：
Fernandez-Gamez, Manuel A.
中科院分区：
文献类型：
--
作者：
Alaminos, David;Ignacio Peliez, Jose;Fernandez-Gamez, Manuel A.
鉴于需要获得融资和建立国际关系，主权债务和货币在任何国家的发展中都发挥着越来越重要的作用。由于主权债务和货币危机在国际经济活动中的极端重要性，金融危机文献中反复出现的主题是对主权债务和货币危机的预测。然而，现有模型的局限性与准确性有关，文献要求对该主题进行更多的调查，并且在所使用的样本中缺乏地理多样性。本文提出了预测主权债务和货币危机的新模型，使用各种计算技术，提高了它们的精度。此外，这些模型提供了世界主要地理区域的广泛全球样本的经验，例如非洲和中东、拉丁美洲、亚洲、欧洲和全球。我们的模型在精度水平上展示了统计计算技术的优越性，这是主权债务危机的最佳方法：模糊决策树、AdaBoost、极端梯度增强和深度学习神经决策树，以及预测货币危机的最佳方法：深度学习神经决策树、极端梯度增强、随机森林和深度信念网络。我们的研究对各国应对金融危机风险的宏观经济政策充分性具有巨大且潜在的重大影响，并提供了可能改善各国金融平衡的工具。
Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.