Predictive Regressions for Measures of Systemic Risk

不断发生的金融危机说明了系统性风险及其预测的重要性。在第一步，该项目将为系统性风险衡量提出新的预测模型。选择合适的预测者尤为重要。文献中提出的预测指标包括通胀、10年期政府债券收益率和股市波动。然而，这些解释变量中的许多随着时间的推移表现出不同程度的依赖性。因此，对这些变量的预测性内容的显著性检验不会保持大小，因此选择合适的统计显著预测值变得不可能。因此，在该项目的第二步中，将开发能够处理具有不同依赖程度的预报器的程序。这应该能够从统计上合理地选择系统性风险的预测指标。然后，第三步阐明解释变量的方差(即变异范围)中的突变对统计上有效的预测值选择的作用。在经济变量(如上文提到的通货膨胀)中，经常可以观察到这种变化的突变，因此是一种与经验相关的现象。因此，将预测者的显著性测试“强化”也是针对方差突变的，这是项目第三部分的任务，这是有意义的。

Constantly recurring financial crises illustrate the importance of systemic risks and their prediction. In a first step, the project will propose new forecasting models for systemic risk measures. The selection of suitable predictors is of particular importance. Predictors proposed in the literature include inflation, 10-year government bond yields and stock market volatility. However, many of these explanatory variables exhibit varying degrees of dependence over time. As a result, significance tests for the predictive content of these variables do not hold size, such that a selection of suitable statistically significant predictors becomes impossible. Therefore, in a second step of the project, procedures are to be developed that can handle predictors with varying degrees of dependence. This should enable a statistically sound selection of predictors for systemic risk. The third step then sheds light on the role of breaks in the variance (i.e., the range of variation) of the explanatory variables on the statistically valid selection of predictors. Such breaks in variation are often observed for economic variables (such as inflation mentioned above) and are therefore an empirically relevant phenomenon. Therefore, it is of interest to "robustify" the significance tests for the predictors also against breaks in variance, which is the task of the third part of the project.