Backtesting Macroprudential Stress Tests

48 Pages Posted: 10 Oct 2020 Last revised: 14 Feb 2022

See all articles by Amanah Ramadiah

Amanah Ramadiah

University College London - Financial Computing and Analytics Group, Department of Computer Science

Daniel Fricke

Deutsche Bundesbank

Fabio Caccioli

University College London

Multiple version iconThere are 2 versions of this paper

Date Written: August 21, 2020

Abstract

Macroprudential stress tests generate a wide range of stress outcomes, depending on the chosen input parameters. Building on the concept of reverse stress tests, we embrace this parameter sensitivity in a backtesting exercise. We generalize an otherwise standard model of price-mediated contagion by interpolating between different liquidation dynamics among banks (leverage targeting vs. threshold dynamics). We then test the capability of this model to match actual bank non-/defaults in the United States for the years 2008--10, where we treat the underlying liquidation dynamics as another free input parameter. While the model performance depends on the type of shock being imposed, we find that all liquidation dynamics we consider can explain to some extent (in particular better than a random benchmark) the pattern of defaults observed during the subprime crisis. We identify the region in the parameter space where a specific dynamic leads to the best fit of the data, and in the most relevant regime (illiquid asset markets and small initial shocks) leverage targeting turns out to provide the most accurate results. We also show how the results depend on the initial shock level, the market impact parameter, on the number of asset liquidation rounds, and the chosen liquidation functions.

Keywords: systemic risk, fire sales, price-mediated contagion, common asset holdings

JEL Classification: G01, G11

Suggested Citation

Ramadiah, Amanah and Fricke, Daniel and Caccioli, Fabio, Backtesting Macroprudential Stress Tests (August 21, 2020). Journal of Economic Dynamics and Control, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3678600 or http://dx.doi.org/10.2139/ssrn.3678600

Amanah Ramadiah (Contact Author)

University College London - Financial Computing and Analytics Group, Department of Computer Science ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Daniel Fricke

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

Fabio Caccioli

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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