We build a balance sheet-based model to capture run risk, i.e., a reduced potential to raise capital from liquidity buffers under stress, driven by depositor scrutiny and further fuelled by fire sales in response to withdrawals. The setup is inspired by the Silicon Valley Bank (SVB) meltdown in March 2023 and our model may serve as a supervisory analysis tool to monitor build-up of balance sheet vulnerabilities. Specifically, we analyze which characteristics of the balance sheet are critical in order for banking system regulators to adequately assess run risk and resilience. By bringing a time series of SVB’s balance sheet data to our model, we are able to demonstrate how changes in the funding and respective asset composition made SVB prone to run risk, as they were increasingly relying on heldto-maturity, aka hidden-to-maturity, accounting standards, masking revaluation losses in securities portfolios. Finally, we formulate a tractable optimisation problem to address the designation of heldto-maturity assets and quantify banks’ ability to hold these assets without resorting to remarking. By calibrating this to SVB’s balance sheet data, we shed light on the bank’s funding risk and impliedrisk tolerance in the years 2020–22 leading up to its collapse.
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