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Systemic risk in the U.S. banking sector during the Silicon Valley bank collapse: a neural network analysis
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Orientador(es)
Resumo(s)
The collapse of Silicon Valley Bank in 2023 exposed significant systemic risks, particularly
for commercial and regional bank. This study analyzes 125 banks, categorized by size, to
evaluate their contributions to systemic risks using a neural network quantile regression with
Value-at-Risk and Conditional Value-at-Risk metrics. Results show that large banks remained
stable, reflecting effective regulation, while commercial and regional banks experienced sharp
increases in systemic risks. The findings highlight the need for enhanced supervision of
smaller banks to mitigate their vulnerability to external shocks and prevent broad financial
instability. The Banks with a dual risk of high vulnerability and risk exposure for the system
are identified as key drivers of systemic risk.
Descrição
Palavras-chave
Systemic risk Neural networks Quantile regression CoVar