摘 要
流动性风险管理是现代金融体系稳定运行的重要保障，尤其是在全球金融市场波动加剧的背景下，如何有效识别、评估和管理流动性风险已成为金融机构亟需解决的核心问题。本研究旨在探讨流动性风险管理策略，并通过实证分析验证其有效性。研究基于巴塞尔协议III框架下的流动性监管指标，结合中国银行业的实际数据，构建了包含流动性覆盖率（LCR）和净稳定资金比率（NSFR）的综合评估模型。同时，引入机器学习算法优化预测精度，以应对复杂多变的市场环境。通过对2015年至2022年间中国上市银行的面板数据分析，研究发现：流动性风险管理策略的有效性不仅依赖于单一指标的监控，还需结合动态情景模拟与压力测试进行前瞻性评估。此外，研究结果表明，机器学习方法在预测流动性风险方面具有显著优势，能够更精准地捕捉市场变化对金融机构流动性的影响。本研究的主要贡献在于将传统金融理论与现代技术手段相结合，提出了一种适用于新兴市场的流动性风险管理框架，为金融机构制定科学合理的风险管理策略提供了理论支持和实践指导。这一创新性方法有助于提升金融机构的抗风险能力和市场竞争力，同时为监管部门完善流动性风险管理政策提供了重要参考。
关键词：流动性风险管理；机器学习；巴塞尔协议III；流动性覆盖率（LCR）；净稳定资金比率（NSFR）
Liquidity Risk Management Strategies and Their Empirical Analysis
Abstract: Liquidity risk management is a critical safeguard for the stable operation of the modern financial system, particularly in the context of intensified global financial market volatility. Effectively identifying, evaluating, and managing liquidity risks has become a core issue urgently requiring resolution by financial institutions. This study aims to explore liquidity risk management strategies and empirically validate their effectiveness. Based on the liquidity regulatory indicators under the Basel III fr amework and incorporating actual data from China’s banking sector, a comprehensive evaluation model was constructed, encompassing the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR). Simultaneously, machine learning algorithms were introduced to optimize predictive accuracy in response to complex and ever-changing market conditions. Through panel data analysis of listed banks in China from 2015 to 2022, the study revealed that the effectiveness of liquidity risk management strategies not only depends on the monitoring of single indicators but also requires forward-looking assessments combined with dynamic scenario simulations and stress tests. Furthermore, the findings indicate that machine learning methods possess significant advantages in predicting liquidity risks, enabling more precise capture of the impact of market changes on financial institutions’ liquidity. The primary contribution of this study lies in integrating traditional financial theories with modern technological approaches to propose a liquidity risk management fr amework applicable to emerging markets. This innovative method provides theoretical support and practical guidance for financial institutions in formulating scientifically sound and reasonable risk management strategies, enhancing their risk-resistance capabilities and market competitiveness. Additionally, it offers crucial references for regulatory authorities in refining liquidity risk management policies.
Keywords: Liquidity Risk Management; Machine Learning; Basel Iii; Liquidity Coverage Ratio (Lcr); Net Stable Funding Ratio (Nsfr)
目 录
一、绪论 1
（一）流动性风险管理的研究背景与意义 1
（二）国内外流动性风险管理研究现状 1
（三）本文研究方法与技术路线 1
二、流动性风险管理策略的理论基础 2
（一）流动性风险的基本概念与特征 2
（二）流动性风险管理的核心框架 2
（三）主要流动性风险管理策略分析 3
三、流动性风险管理策略的设计与实施 3
（一）流动性风险评估方法与工具 4
（二）基于情景分析的风险管理策略设计 4
（三）实施过程中的关键问题与解决方案 5
四、流动性风险管理策略的实证分析 5
（一）实证研究的数据与样本选择 5
（二）流动性风险管理策略的效果检验 6
（三）实证结果分析与优化建议 6
结论 7
参考文献 8
致 谢 9
