Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests.

翻译：预测风险（以分位数衡量）和系统性风险（以Adrian和Brunnermeier（2016）提出的CoVaR衡量）在经济学和金融学中具有重要意义。然而，以往研究表明，预测关系可能随时间推移而不稳定。因此，本文开发了用于预测分位数回归和CoVaR回归的结构性断点检验方法。这些检验能够检测协变量预测能力的变化，并基于自归一化原理构建。我们证明，无论预测变量是平稳的还是接近平稳的，所提出的检验均有效，这使得该检验适用于广泛的实践应用。模拟实验展示了检验方法在有限样本下具有良好的性质。针对股权溢价和系统性风险预测模型的两个实证应用，验证了该检验方法的实用性。