Predictive Regressions for Measures of Systemic Risk

不断出现的金融危机说明了系统性风险及其预测的重要性。第一步，该项目将为系统性风险措施提出新的预测模型。选择合适的预测因子尤为重要。文献中提出的预测指标包括通货膨胀、10年期政府债券收益率和股市波动。然而，随着时间的推移，许多这些解释变量表现出不同程度的依赖性。因此，对这些变量的预测内容的显著性检验不能保持大小，这样就不可能选择合适的统计显著预测因子。因此，在项目的第二步中，将开发能够处理具有不同依赖程度的预测因子的程序。这将使系统性风险的预测因素选择在统计上合理。然后，第三步阐明了解释变量的方差（即变异范围）在预测因子的统计有效选择中的作用。这种变化的中断经常在经济变量（如上面提到的通货膨胀）中观察到，因此是一种与经验相关的现象。因此，对预测因子的显著性检验进行“鲁棒化”是很有意义的，这也是项目第三部分的任务。

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.