  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 fueled 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 held-to-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 held-to-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 implied risk
tolerance in the years 2020--22 leading up to its collapse.
