Enhancing banking systemic risk indicators by incorporating volatility clustering, variance risk premiums, and considering distance-to-capital

We develop a systemic risk indicator approach using a structural GARCH option-based default risk framework incorporating volatility clustering, variance risk premiums, along with distance-to-capital features. We apply our model to the U.S. banking sector, testing its explanatory and forecasting power. Our model successfully identifies the most systemically risky banks during heightened systemic-risk episodes. Comparing our results to related approaches, especially the respected indicator of the Federal Reserve Bank of Cleveland, we evidence markedly improved performance. Given the recent implosion of Silicon Valley Bank, exploring new approaches to constructing banking systemic risk indicators should be of great interest to regulators and policy makers.