Predictive Regressions for Measures of Systemic Risk

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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.