中国银行业系统性风险预警研究——基于SVM模型的建模分析

A Research on Banking Systemic Risk Prediction in China——A Modeling Analysis Based on Support Vector Machines

采用支持向量机和核主成分分析法构建中国银行业系统性风险预警模型,将预警结果与BP神经网络模型和Logit回归模型的预警结果进行对比,并基于2008年1月~2017年9月的数据,采用SVM预警模型预测2009年1月~2018年9月中国银行业系统性风险水平。研究结果显示:与BP神经网络和Logit回归模型相比,SVM模型具有较高的预警正确率;在不同的阶段中国银行业系统性风险水平呈现出不同的变动趋势。建议中国政府部门和银行业警惕资本市场泡沫增长等隐性风险,不断完善银行业内部系统的风险防控机制,持续强化银行业宏观审慎监管。

The paper applies support vector machines ( SVM) and kernel principalcomponent analysis to construct a banking systemic risk prediction model in China, andcompares the result with that of BP neural networks and Logit regression. Then, the paperpredicts China's banking systemic risk during January 2009 to September 2018 by using the SVM prediction model based on the data from January 2008 to September 2017. Theresearch demonstrates that: by comparison, the SVM model has higher prediction accuracythan BP neural networks and Logit regression;the China's banking systemic risk showsdifferent trends at different stages. It is suggested that Chinese government and banking industryshould be alert to various hidden risks such as bubble growth of capital market,continuously improve the prevention and control mechanism of financial risk and constantlystrengthen the macro-prudential supervision in banking industry.