Tail risk dynamics of banks with score-driven extreme value models

This paper proposes a new class of marked point process models to capture the clustering behavior in extreme financial events. The idea of multiple dynamic parameters embedded in the context of score driven models is utilized to estimate a dynamic extreme value approach, labeled as the Orthogonal Score-Driven Peaks Over Threshold model. A Monte-Carlo study is conducted to study different time-varying parameter specifications. The results show that this approach can capture a range of different dynamics for the parameters. In an empirical application, we study the dynamics of the tail distribution over time, and in particular on VaR and ES forecasts, for the constituents of the S&P Banks Index. Finally, we study the behavior of extremely adverse returns in the financial system by means of a decomposition of the tail-$\beta$ risk measure, giving a deeper understanding of both the dynamics of the risk of an individual bank, and the systemic linkages associated with the stability of the global financial system.