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银行宏观安全状态指数及预警监测分析

发布时间:2018-08-17 15:27
【摘要】:银行安全问题一直是政府和金融经济界高度关注的问题,也是学者们长期研究的重点内容之一。本文根据我国的现实情况,结合国内外相关问题的相关研究成果,分别建立了银行宏观安全状态指标体系和银行宏观安全预警指标体系,采用综合评价法对中国2003-2012年的月度数据进行估算,并在此基础上,选择三种不同的识别标准建立Logit预警模型,结果显示模型是稳定的。分析发现:国内信贷增长量/GDP、短期外币贷款/外汇储备与银行宏观安全具有显著的正向关系,广义货币/外汇储备、M2/GDP、净出口与银行宏观安全有较显著的负向关系,具有较稳定的预警影响作用。广义货币/外汇储备对银行宏观安全的影响时间为3个月,M2/GDP和净出口对银行宏观安全的影响时间为2个月,其余变量对银行安全的影响为1个月。通过银行宏观安全状态指数MacroBSI和基于Logit的银行宏观预警模型,可进一步揭示银行宏观安全状态与一些宏观影响指标的动态规律,并提出合理的建议,供政府决策部门参考。
[Abstract]:The problem of bank security has been highly concerned by the government and the financial and economic circles, and has also been one of the important contents of scholars' long-term research. According to the reality of our country, combined with the related research results at home and abroad, this paper establishes the macro security state index system of banks and the early warning index system of bank macro security respectively. The monthly data of China from 2003 to 2012 are estimated by comprehensive evaluation method. On this basis, three different recognition criteria are selected to establish the Logit early warning model. The results show that the model is stable. It is found that there is a significant positive relationship between short-term foreign currency loan / foreign exchange reserve and bank macro security, while broad currency / foreign exchange reserve M 2 / GDP. net export has a significant negative relationship with bank macro security. It has a more stable early warning effect. The influence time of broad money / foreign exchange reserve on bank macro security is 3 months M 2 / GDP and 2 months of net export to bank macro security, and the other variables influence bank security for 1 month. Based on the bank macro-security state index (MacroBSI) and the bank macro-warning model based on Logit, the dynamic law of bank macro-security state and some macroscopical impact indexes can be further revealed, and reasonable suggestions are put forward for the reference of government policy-making department.
【作者单位】: 暨南大学经济学院;广东省大数据分析与处理重点实验室;暨南大学管理学院;
【基金】:国家社会科学基金一般项目(16BTJ035) 广东省自然科学基金项目(2016A030313108)
【分类号】:F831.51

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