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Quantifying and Stress Testing Operational Risk with Peer Banks’ Data

Azamat Abdymomunov () and Filippo Curti ()
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Azamat Abdymomunov: Federal Reserve Bank of Richmond
Filippo Curti: Federal Reserve Bank of Richmond

Journal of Financial Services Research, 2020, vol. 57, issue 3, No 3, 287-313

Abstract: Abstract One of the main challenges that banks face in quantifying operational risk is the instability of risk estimates caused by heavy-tailed and insufficient loss data. To address these issues, we propose a loss scaling method to combine a bank’s internal loss data with loss data of peer banks. In this method, we scale tail losses using total assets and a measure of risk management quality as scaling factors. Using supervisory operational loss data from large U.S. bank holding companies, we demonstrate that our method of incorporating scaled external data improves the stability of operational risk estimates. In addition, we show that our scaling method can be applied for stress testing operational losses to macroeconomic shocks by better depicting the relationship between losses and macroeconomic variables.

Keywords: Operational risk; Banking Capital; Stress testing (search for similar items in EconPapers)
JEL-codes: C22 C23 G21 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10693-019-00320-w

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