Ramadiah, A;
Fricke, D;
Caccioli, F;
(2022)
Backtesting macroprudential stress tests.
Journal of Economic Dynamics and Control
, 137
, Article 104333. 10.1016/j.jedc.2022.104333.
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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.
Type: | Article |
---|---|
Title: | Backtesting macroprudential stress tests |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.jedc.2022.104333 |
Publisher version: | https://doi.org/10.1016/j.jedc.2022.104333 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Social Sciences, Economics, Business & Economics, Systemic risk, Fire sales, Common asset holdings, Backtesting, SYSTEMIC RISK, FINANCIAL STABILITY, CONTAGION, MODELS, SALES |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10147638 |
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