Predicting systemic financial risk is essential for understanding the financial system's stability and early warning of financial crises. In this research, we use the financial stress index to measure systemic financial risk. We construct the stress index for five financial submarkets and composite stress index, employ the Markov regime switching model to identify the systemic financial risk stress state. On this basis, we use interpretable machine learning models to forecast systemic financial risk, analyze and compare the results of the intrinsic interpretable machine learning models and the post-hoc explainable methods. The results indicate that systemic financial risk can be effectively predicted using both the submarket stress index and the feature variables, with the submarket stress index as the independent variable providing relatively higher accuracy. There is a linearly positive relationship between the stress index of each submarket and systemic financial risk, with financial stress in the stock and money markets having the greatest impact on systemic financial risk. For each feature variable, stock–bond correlation coefficient, stock valuation risk, the maximum cumulative loss of the SSE Composite Index (SSE CMAX), and loan-deposit ratio have strong predictive power. Our research can provide reference for government to construct prediction model and indicator monitoring platform of systemic financial crisis.

预测系统性金融风险对于了解金融体系的稳定性和金融危机的预警至关重要。在本研究中，我们使用财务压力指数来衡量系统性金融风险。构建五个金融子市场的压力指数和综合压力指数，运用马尔可夫政权切换模型识别系统性金融风险压力状态。在此基础上，我们利用可解释的机器学习模型来预测系统性金融风险，并对内在可解释机器学习模型和事后可解释方法的结果进行分析和比较。研究结果表明，子市场压力指数和特征变量可以有效预测系统性金融风险，其中子市场压力指数作为自变量具有较高的准确度。各子市场的压力指数与系统性金融风险之间存在线性正相关关系，其中股票市场和货币市场的金融压力对系统性金融风险的影响最大。对于各个特征变量，股债相关系数、股票估值风险、上证综指最大累计损失（上证CMAX）、贷存比等都具有较强的预测能力。我们的研究可为政府构建系统性金融危机预测模型和指标监测平台提供参考。