基于贝叶斯模型与机器学习算法的金融风险网络评估模型

Estimate model based on Bayesian model and machine learning algorithms applicated in financial risk assessment

本文以贝叶斯方法为基础构建了用于估计银行间负债的模型,并利用机器学习算法构造了可在条件分布基础上进行抽样的Gibbs取样器,抽样被用于压力测试,以此给出所有可能测试结果的概率。最后,推导出了银行的违约概率并讨论其对包含在网络模型中的先验信息的敏感性,帮助金融监管部门评估金融机构的违约风险,减少系统性金融风险,维护金融市场的稳定。

A model to estimate the inter-bank liabilities was construct based on the Bayesian method,and then use machine learning algorithms to construct a Gibbs sampler to sample on the basis of conditional distribution. The sampling is used for stress testing and give the probability of all possible test results.Finally,as a model application,this paper will derive the bank’s default probability and discuss its sensitivity to a priori information contained in the network model,helping financial regulators assess financial institutions’ default risk,reduce systemic financial risks and maintainfinancial market stability.