22 November 2023

SUERF: Banks’ credit forecasts excel at rank ordering but miss the evolution of losses

Consistent with the use of through-the-cycle risk estimates for regulatory purposes, EL rates rarely evolve in line with AL rates, which helps explain a large precautionary element in Basel III capital requirements.

Focusing on credit risk, we compare banks’ expected loss (EL) rates, collected confidentially by the Basel Committee on Banking Supervision from 2009 to 2022, and the corresponding actual loss (AL) rates. Consistent with the use of through-the-cycle risk estimates for regulatory purposes, EL rates rarely evolve in line with AL rates, which helps explain a large precautionary element in Basel III capital requirements. By contrast, the rank-order of EL rates across banks matches closely that of AL rates, in line with recent and forthcoming regulatory efforts to improve risk-measurement practices. We also find that EL rates are relatively conservative for banks with higher valuations and are more likely to be optimistic on the heels of higher bank profitability and financial overheating, as captured by the credit-to-GDP gap.

1. Banks’ resilience hinges on credit risk measurement and management

With borrowing and lending at the core of bank business models, credit assessments are essential for banks’ risk management. Credit losses featured prominently in the great financial crisis (GFC) (eg Claessens et al (2010)). In recent years, credit risk accounted for 60% to 80% of the capital requirements for internationally active banks (BCBS (2023), p 50–51). On the back of significant debt accumulation during the low-for-long era, debt service costs are rising as policy rate hikes feed into corporate bond yields and lending rates. Under plausible scenarios, the rise of these costs may drive credit losses up to GFC levels (BIS (2023)). Ultimately, accurate credit loss forecasts and/or regulatory conservatism – ie larger precautionary elements in the mapping from the forecasts to capital requirements – need to ensure enough resources for absorbing credit losses.

2. Confidential supervisory data sheds light on banks’ credit forecasts

We assess the accuracy of banks’ credit loss forecasts – ie expected loss (EL) rates. We juxtapose these EL rates with banks’ actual loss (AL) rates and identify drivers of the discrepancies. At the heart of the exercise are exclusive confidential supervisory data, collected by the Basel Committee on Banking Supervision (BCBS). These data contain one-year EL rates, as reported by 65 internationally active banks to supervisors from end‑2008 to end-2022. Combining EL rates on non-defaulted exposures with vendor accounting data on AL rates, we answer two questions:

1. How well do EL rates capture the evolution of AL rates?
2. Does the rank-order of EL rates across banks align with that of AL rates?
   
   

3. Findings: two faces of banks’ credit loss forecasts

The performance of EL rates differs starkly between the time and cross-section dimensions.

EL rates generally fail to capture the time profile of AL rates. Concretely, the correlation of year‑to‑year changes in the two series is statistically significant for only 15% of the banks, reflecting divergencies as regards spikes and trends (Graph 1). This implies that a conservative mapping from EL rates to capital requirements is needed to ensure enough loss‑absorbing resources at each point in time. We estimate that – over our sample period – such a mapping would have resulted in capital requirements being (at least) twice as large on average as yearly AL for three-quarters of the banks (Graph 1.A). Actual capital requirements exhibit stronger conservatism, not least because real-life uncertainties are higher than those underpinning our stylised exercise.

By contrast, banks fare well when it comes to signalling the relative riskiness of their credit portfolios. The only exception is in 2021, in line with the extraordinary nature of the Covid-19 pandemic and related support measures. For all other years in the sample, the rank-ordering of EL rates across banks closely matches that of the corresponding AL rates (Graph 2)...

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By Martin Birn, Renzo Corrias, Christian Schmieder and Nikola Tarashev
Bank for International Settlements

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