Financial uncertainties drive extreme risks in China

Abstract

The identification of primary financial uncertainties driving extreme risks is a challenging task due to the unobservability of uncertainty and pseudo-significance in data-rich environments. While previous study mainly focuses on specific uncertainty that tightly linked to specific theoretical notions or limited datasets and its driving ability to the systemic risk in the financial markets, this study investigates the impacts of a bundle of financial uncertainties on extreme risks using a robust empirical framework. By constructing financial uncertainty indices that exclude the entire forecastable component from a big data sample set and assessing extreme financial risk through Expected Shortfall (ES), then identifying multiple financial uncertainties that significantly influence extreme risks. The findings indicate that China's financial uncertainties volatile with greater magnitude and less frequently compared with the extreme risks. From statical perspective, uncertainties from the central bank, deposit and loan, foreign exchange, and stock systems are primary robust drivers of extreme risks during the overall sample period. Dynamically, the robust positive relationship between multiple financial uncertainties and risks shifts from strong to weak and then a partial rebound. The uncertainty of deposit and loan systems act as a more significant driver during the global financial crisis period, while the uncertainty of stock and bond systems are more dominant in the epidemic period. These results emphasize the importance of considering different types of financial uncertainties and time-varying features when assessing financial stability, providing valuable insights for policymakers and financial market participants for risk management.