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宏观变化、银行结构与流动性风险——基于流动性覆盖率LCR的实证分析 被引量:7

Macro Shocks, Banking Structure and Liquidity Risk:An Empirical Analysis based on LCR
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摘要 流动性风险是商业银行面临的最重要、最致命、最隐蔽的风险之一。本文基于内外部因素分析框架,运用压力情景下的LCR作为风险评价指标,通过构建VAR计量模型,借助脉冲响应函数和方差分解方法,定量验证了宏观变化、结构调整对大型、中型银行流动性的冲击效应及贡献度大小。研究发现,外部宏观因素对银行流动性风险的影响增强。其中,大型银行得益于被动负债和中央银行的救助优势,对自身资产负债结构摆布及流动性风险管控能力更强;中型银行由于同业及表外业务过快扩张,流动性风险更为突出。据此,宏观层面要更加注重宏观流动性把控,强化金融监管行为的有机协调;微观上则应进一步提升银行流动性风险管理能力,加强同业、表外业务的全面流动性管理与监管。 Liquidity risk is one of the most important, deadly and hidden risks to Banks. This paper develops a VAR model based on the framework of internal and external factor analysis and using LCR as the risk evaluation index. This model measures the impacts and contribution of macro shocks and banking structural adjustment on the liquidity of large and medium-sized banks by impulse response function and variance decomposition.We find that, external macro factors have stronger effect son the liquidity risk of banks. The liquidity risk of large banks can be effectively contained with the advantage of passive leverage and rescues from the Central Bank. The liquidity risk of the medium sized banks is more severe due to the expansion of the interbank and off-balance-sheet activities. So we should pay more attention to macro liquidity, the coordination of financial regulators, and the liquidity risk management ability of banks, especially the comprehensive liquidity risk management and supervision on interbanks business and off-balance-sheet businesses.
出处 《金融监管研究》 2017年第8期1-17,共17页 Financial Regulation Research
关键词 商业银行 流动性风险 VAR模型 脉冲响应函数 方差分解 Banks Liquidity Risk VAR Impulse Response Function Variance Decomposition
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