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经济增长状态与银行系统性风险——基于马尔科夫区制转移的CCA模型 被引量:8

Economic Growth State and Systemic Risk in Banking Sector——Based on Markov Regime Switching CCA Model
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摘要 金融系统性危机的发生与经济增长状态变化紧密相关,外部冲击和内部脆弱性是金融部门系统性风险发生的两个来源。已有研究在分析经济增长对金融部门系统性风险的影响时一般将经济增长视为外部冲击,忽视了系统性风险度量模型已经内生化了经济增长状态影响的问题,使用的系统性风险指标不能将外部冲击和内部脆弱性引发的风险进行区分,难以判断金融部门系统性风险的来源,而且无法准确判断经济增长对于系统性风险的影响程度。将马尔科夫区制转移模型引入或有权益分析方法分析框架,计算经济增长状态的转移概率与银行部门违约距离的相关概率,得到违约距离的无条件概率分布,剥离经济增长对银行系统性风险的影响,构造反映银行内部脆弱性风险的无条件违约距离,实现对银行部门系统性风险的分解。选取2007年1月至2016年12月希腊和中国上市银行整体作为样本,分别考察外部冲击和内部脆弱性对银行部门系统性风险的影响。研究结果表明,银行部门系统性风险可以分解为内部脆弱性风险和外部经济状态影响,该系统性风险主要由内部脆弱性决定,经济增长对系统性风险的作用类似于增效器。经济增长下行状态会导致银行部门系统性风险急剧上升,而经济增长上行状态能改善银行部门系统性风险状况。经济增长状态对系统性风险的放大效果具有不对称性。银行部门内部脆弱性风险状况出现恶化时,经济增长状态的增效作用更强烈;而银行部门内部脆弱性风险状况得到改善时,经济增长状态的增效作用相对温和。短期内救助政策通过外部机制影响系统性风险,危机状态下救助政策能够对系统性风险发挥明显的稳定作用。在宏观审慎监管中,不仅要求银行部门降低内部脆弱性风险,而且重视外部经济条件和实体经济部门与系统性风险的联系,建立前瞻性的逆周期监管机制,实现对系统性风险的有效管理。当银行部门陷入危机时,政府部门应及时提供有效救助。 The occurrence of financial crisis is closely related to the change of economic growth. External shock and internal vul- nerability are two sources of systemic risk in the financial sector. In the analysis of the impact of economic growth on systemic risk in the financial sector, existing research often sees economic growth as an external shock, ignoring the problem that the sys- temic risk measurement model has endogenized the impact of economic growth, and the systemic risk indicators they used can not distinguish between external shock risks and the risks posed by internal vulnerabilities, making it difficult to determine the source of systemic risk in the banking sector and to analyze the extent to which economic growth affects systemic risk. The Markov regime switching model is introduced into the contingent claim analysis framework to calculate the associated probabilities of the economic growth state and the distance of default to obtain the unconditional probability distribution of the dis- tance of default, to strip the influence of the economic growth on the systemic risk, and to construct the unconditional distance of default of banking sector's intemal vulnerability risk, and then can decompose the systemic risk of banking sector. This paper chooses the listed banks in Greece and China as samples from January 2007 to December 2016 to examine the impacts of external shocks and internal vulnerabilities on the systemic risk of banking sector. The results show that systemic risk of banking sector can be broken down into intemal vulnerability risk and the impact of external economic state. Based on the empirical results of domestic and foreign banking departments, the research shows that sys- temic risk of banking sector is mainly determined by its own fragility, and the state of economic growth functions as a muhiplier for systemic risk. The downward trend of economic growth will lead to a sharp rise in systemic risk in banking sector, while the upward trend can further improve the risk situation of banking sector. The amplification effect of economic growth on systemic risk is asymmetrical. When banking sector's vulnerability risk situation is deteriorated, multiplier effect of economic growth downward state is stronger; and when it's improved, the synergistic effect which economic growth state has on systemic risk is relatively modest. In the short term, bank bailouts affect systemic risk "through external mechanism and can stabilize the systemic risk obvi- ously in crisis. In macroprndential supervision, regulators should not only ask the banking sector to reduce their own risks, but also pay at- tention to the links between external economic conditions in addition to real economy and systemic risk. Forward-looking counter- cyclical supervision mechanism should be established to achieve effective management of systemic risk. When banking sector is in crisis, the government should provide effective bailouts timely.
出处 《管理科学》 CSSCI 北大核心 2017年第6期19-32,共14页 Journal of Management Science
基金 国家社会科学基金(15BJY152) 教育部人文社会科学研究基金(14YJC790100)~~
关键词 经济增长 系统性风险 内部脆弱性 马尔科夫区制转移模型 或有权益分析 违约距离 economic growth systemic risk internal vulnerability Markov regime switching model contingent claim analysis distance to default
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