Predicting Asia Sovereign Debt Crises Based on Support Vector Machine

摘要：

This paper designs and develops an Asia sovereign debt crises predictive model based on support vector machine. A three year time window is defined to observe debt crises probability. Taking 173 samples from eighteen emerging market countries in Asia, the empirical work reveals that the model can predict sovereign debt crises in next three years with accuracy 89.19％. Data analysis also shows repayment history, reserve/MGS ratio and accumulated arrear ratio are top three factors related to debt crisis status.

关键词： sovereign default forcasting sovereign debt crisis support vector machine

作者: Hong Kang Qing Shan Deng

作者单位: School of software and communication engineering Jiangxi University of Finance & Economics Nanchang, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 453-457

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）