我国货币市场基金起步较晚，但发展速度极快，规模一跃成为全球第二。研究我国货币市场基金对银行体系流动性风险的影响机制，对于厘清影子银行和传统银行体系关联具有较强的理论价值和政策意义。本文试图回答以下问题：第一，货币市场基金发展对银行流动性风险生成产生何种冲击效应？具体而言，货币市场基金规模扩张和流动性错配程度对银行流动性风险产生何种影响？分别通过哪种渠道发挥作用?第二，货币市场基金发展对银行流动性风险传染产生何种影响？包含货币市场基金的同业金融市场稳定性如何？本文通过理论建模和实证检验等方法，对上述问题进行探索：第一，基于2009-2021年114家商业银行的非平衡面板数据，通过固定效应模型、中介效应模型实证分析货币市场基金规模扩张对银行流动性风险的影响机制和渠道检验。研究发现：货币市场基金规模每扩张1个百分点，当期商业银行流动性风险提升1.8%，下一期商业银行流动性风险提升2.0%。货币市场基金扩张推升银行同业负债占比，间接推升银行流动性风险。第二，基于货币市场基金季度持仓数据和同业存单交易数据，构造持有同业存单的货币市场基金流动性错配指标，通过固定效应模型实证发现持有同业存单的货币市场基金流动性错配程度越高，下一季度同业存单价格波动越剧烈，2016年年底货币市场基金危机期间这种影响机制更显著。进一步地，同业存单价格波动率越高，银行体系流动性风险越大。第三，基于复杂网络理论，构建包含货币市场基金在内的同业金融网络。通过分析网络拓扑性质发现，2015年以来货币市场基金已经成为同业金融网络核心。通过网络传导分析法，发现单一银行破产或在此基础上叠加银行体系流动性紧张时，货币市场基金的传染效应引发更多银行破产，特别是同业负债依赖货币市场基金的银行更容易受到风险传染。要防范和化解银行体系流动性风险，建议推进利率市场化改革、推动货币市场基金回归资管产品本源、降低货币市场基金流动性错配程度、限制银行同业负债来源于货币市场基金比例、将货币市场基金纳入宏观审慎监管框架。

China‘s money market funds（MMFs）started relatively late, but developed rapidly and has become the world‘s second-largest. Studying the impact mechanism of China‘s MMFs on bank liquidity risk has an important theoretical value and policy significance in clarifying the relationship between the shadow banking system and the traditional banking system. This article attempts to answer the following questions: First, what impact does the development of MMFs have on the generation of bank liquidity risk? Specifically, what is the impact of the expansion of the MMFs’ scale and the liquidity mismatch of MMFs on bank liquidity risk? And through what channels does it work? Second, what impact does the development of MMFs have on the contagion of bank liquidity risk? How stable is the interbank financial market that includes MMFs? This article explores the above issues through theoretical modeling and empirical testing.First, based on unbalanced panel data from 114 commercial banks from 2009 to 2021, through fixed effects and mediation effect models, the impact mechanism and channel of MMFs’ scale expansion on bank liquidity risk are empirically analyzed. The study found that for every 1% expansion of the MMFs, the current liquidity risk of commercial banks will increase by 1.8%, and the next period‘s liquidity risk of commercial banks will increase by 2.0%. The expansion of MMFs pushes up the proportion of interbank liabilities, indirectly pushing up bank liquidity risk.Second, based on quarterly holding data of MMFs and interbank deposit transaction data, a liquidity mismatch indicator of MMFs holding interbank deposits is constructed. Through a fixed effects model, it is empirically found that the higher the liquidity mismatch of MMFs holding interbank deposits, the more volatile the interbank deposit prices will be in the next quarter. This impact mechanism is more significant during the MMFs crisis period at the end of 2016. Furthermore, the higher the volatility of interbank deposit prices, the greater the liquidity risk of the banking system.Third, based on complex network theory, a interbank financial network including MMFs is constructed. By analyzing the network topology, it is found that since 2015, MMFs have become the core of the interbank financial network. Through network transmission analysis, it is found that when a single bank goes bankrupt or when the banking system is under liquidity pressure, the contagion effect of MMFs can trigger more bankruptcies, especially for banks that rely on interbank liabilities from MMFs.To prevent and resolve liquidity risks in the banking system, it is suggested to promote interest rate marketization reform, promote the return of MMFs to the original source of asset management products, reduce the liquidity mismatch of MMFs , restrict the proportion of interbank liabilities of banks from MMFs, and include MMFs in the macro-prudential supervision framework.