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Zachary Feinstein
- 6 August 2024
- WORKING PAPER SERIES - No. 2970Details
- Abstract
- 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.
- JEL Code
- C62 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Existence and Stability Conditions of Equilibrium
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
- 13 April 2023
- WORKING PAPER SERIES - No. 2806Details
- Abstract
- Interconnectedness is an inherent feature of the modern financial system. While it con-tributes to efficiency of financial services, it also creates structural vulnerabilities: pernicious shock transmission and amplification impacting banks’ capitalization. This has recently been seen during the Global Financial Crisis. Post-crisis reforms addressed many of the causes of this event, but contagion effects may not be fully eliminated. One reason for this may be related to financial institutions’ incentives and strategic behaviours. We propose a model to study contagion effects in a banking system capturing network effects of direct exposures and indirect effects of market behaviour that may impact asset valuation. By doing so, we can embed a well-established fire-sale channel into our model. Unlike in related literature, we relax the assumption that there is an exogenous pecking order of how banks would sell their assets. Instead, banks act rationally in our model; they optimally construct a portfolio subject to budget constraints so as to raise cash to satisfy creditors (interbank and external). We assume that the guiding principle for banks is to maximize risk-adjusted returns gener-ated by their balance sheets. We parameterize the theoretical model with publicly available data for a representative sample of European banks; this allows us to run simulations of bank valuations and asset prices under a set of stress scenarios.
- JEL Code
- C62 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Existence and Stability Conditions of Equilibrium
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages