基于深度强化学习驱动藤Copula方法的农地经营权抵押贷款风险变量关联结构分析

A Deep Reinforcement Learning-driven Vine Copula Method for Correlation Structure Analysis of Mortgage

控制风险是发挥金融核心作用、有效服务实体经济高质量发展的关键,而影响金融风险的特征变量间的关联关系分析是风险溯源和防控的基础.农地经营权抵押贷款作为农村金融的改革创新,研究其违约风险特征变量关联结构对有效降低风险、助力更大范围内推行金融创新、破解农民贷款“风控难”的问题具有重要意义.然而,农地经营权抵押贷款的风险影响因素多变,且其特征变量之间的关联结构组合具有高维复杂性,因此需要一种有效的建模方法.本文提出了一种基于藤copula中C-vine copula函数的深度强化学习驱动算法框架,针对农地经营权抵押贷款风险影响多变量关联结构组合的高维复杂性,实现自动化变量关联结构建模.在此模型中,C-vine copula函数采用二元函数组合,能够方便直观地描述变量间的结构关联.而深度强化学习因具备突出的非线性拟合与高维空间表征能力,在探索尝试中自动学习,在复杂高维度的变量结构关联建模方面发挥关键作用.同时,该方法根据数据分布选择各层级变量及copula函数种类,能有效提升度量模型的效果.研究结果表明,在农地经营权抵押贷款债务违约影响变量的关联结构中,变量生成顺序依次为:贷款金额、贷款利率、抵押土地面积、家庭支出水平、主要农作物产值、家庭收入水平、年龄、受教育年限以及村庄到最近土地交易中心距离.我们还发现,关注变量间尾部的依赖关系对于风险全面分析与有效防范至关重要.本文提出的方法为农地经营权抵押贷款关联变量结构建模提供了支撑,对有效控制农村金融债务违约风险具有重要意义.

Controlling risk is the key to playing a core role in financial services and effectively serving the high-quality development of the real economy.And the correlation analysis between the characteristic variables is the foundation of risk tracing and prevention and control.As the reform and innovation of rural finance,the mortgage loan of agricultural land management right is important to reduce risk effectively and promote financial innovation to a larger scope and solve the problem of control difficulty for peasant loans by studying its risk characteristic variables associated structure.However,there are multiple variable risk factors affecting the mortgage loan debt default of agricultural land management right,and the combined correlation structure of its characteristic variables has high-dimensional complexity.Therefore,an effective modeling method is needed.To this end,this paper proposes a deep reinforcement learning-driven algorithm framework based on the C-vine copula function in vine copula.In our model,the C-vine copula function uses a binary function combination to conveniently and intuitively describe the structural correlation between variables.Deep reinforcement learning,with outstanding nonlinear fitting and high-dimensional space representation capabilities,automatically learns in exploration,and plays a key role in the modeling of complex high-dimensional variable structure correlations.According to the distribution of data,the variables and copula function types at each level are selected to effectively improve the total log-likelihood of the model.The results show that in the correlation structure of the variables influencing the default of the mortgage loan debt of the agricultural land management right,the generation order of the variables is loan amount,interest rate,mortgaged farmland area,household’s expenditure,output value of major crops,age,household’s income and the distance from village halls to the nearest farmland trading center.We also found that paying attention to the dependency relationships among the tails of variables is crucial for comprehensive risk analysis and effective prevention.This paper sheds light on an intelligent modeling method of reinforcement learning-driven vine copula to mine the correlation variable structure of farmland mortgage loans,which provides support and has important significance for effectively controlling the rural financial debt default risk.