Sectoral spillovers and systemic risks: Evidence from China

The recent collapse of Silicon Valley Bank prompts interest in how risks are assessed. We develop a new approach to estimating the systemic importance of sectors, integrating a 'top-down' and 'bottom-up' informed spillover network through component-expected-shortfall and generalizederror-variance-decomposition methodologies. This approach identifies systemically important sectors by combining too interconnected to fail and too big to fail logics. We find that levels of risk spillovers in respective sectors are not typically well correlated with corresponding levels of risk contribution. Consequently, focusing only on connectedness between assets seriously distorts risk estimation. Results are significant for regulatory authorities to accurately identify sector risks.

Automated summary

The collapse of Silicon Valley Bank has called attention to the assessment of risks. This study proposes a new approach to estimate the systemic importance of different sectors by integrating both top-down and bottom-up information networks. Through the use of component-expected-shortfall and generalized error-variance-decomposition methodologies, this approach identifies systemically important sectors based on both interconnectedness and size. The study finds that the level of risk spillovers in sectors is not well correlated with their corresponding level of risk contribution, suggesting that focusing solely on connectedness between assets can distort risk estimation. These findings have significant implications for regulatory authorities in accurately identifying sector risks.