The not-so-hidden risks of 'hidden-to-maturity' accounting: on depositor runs and bank resilience

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 we apply our model to assess the build-up of balance sheet vulnerabilities before its default. More generally, we analyze which characteristics of the balance sheet are critical 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 accounting standards, masking revaluation losses in securities portfolios. Next, we formulate a tractable optimization 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. We conclude by validating our model on the balance sheets of First Republic Bank, US Bancorp, and PNC Financial Services Group Inc.