07 December 2020

BIS: Tools for managing banking distress: historical experience and lessons for today

We analyse the effectiveness of policy tools for large-scale banking distress and draw lessons for today. The depth of recessions following banking distress depends both on the speed with which tools were deployed and their type and on the macro-financial vulnerabilities.

While, in general, swifter and broader-ranging policy actions mitigate such recessions, central banks' asset purchases and lending are particularly effective when banks have been underperforming or when distress follows abnormally large asset price movements, such as those triggered by the Covid19 crisis. Our analysis confirms that the recently employed policies have supported the real economy.¹

Banking distress and crises tend to be followed by deep recessions. The basic reason is the sharp contraction in lending following a breakdown in financial intermediation. Baron et al (2020) document that after episodes of large bank stock price declines and an abnormal number of bank failures - that is, after distress episodes - real GDP falls by 5.5% on average from peak to trough. Output losses are particularly large when distress morphs into a full-scale banking crisis (Laeven and Valencia (2013)). And they vary across countries. For example, during the Great Financial Crisis (GFC) output fell from peak to trough by 0.16% in Switzerland and by almost 30% in Greece.

There are two interrelated sets of explanations for such variations across countries and episodes. One relates to the initial economic conditions, notably the macro-financial imbalances with which countries enter a period of distress. For instance, banking distress associated with the unravelling of a domestic financial imbalance (eg a housing bubble) may have a very different impact than that stemming from an external event in the absence of such or similar imbalances (eg a crisis imported through cross-border exposures).² The other set of explanations relates to the policies employed. The timing and degree of policy activity and the specific tools deployed (eg central bank lending, separation of impaired assets) differ considerably across episodes. These choices will influence the severity of the recession, not least if the effectiveness of tools varies with the initial conditions.

While many studies document the economic consequences of banking distress, relatively few systematic cross-country analyses explore the effectiveness of mitigating policy tools.³ Progress has been hamstrung by a lack of comprehensive data about these tools. Furthermore, it is inherently difficult to measure effectiveness. For example, larger-scale distress calls for stronger interventions, but also makes success less certain. This may lead to the spurious conclusion that interventions are less effective.

With this special feature, we make progress along three dimensions. First, we classify 62 banking distress episodes into five categories, on the basis of initial macro-financial vulnerabilities.⁴ Second, using a new database by Adler and Boissay (2020) on the deployment of various distress mitigation tools, we assess empirically whether variations in the evolution of GDP across similar episodes can be explained by differences in the speed and type of policy interventions. Third, we draw policy lessons for today from our analysis.

We find that swifter and broader-ranging policy actions mitigate the impact of banking distress on economic activity. We refine this result in several ways. Central bank lending schemes are more effective in helping restore GDP growth when set up in the first year of distress, whereas impaired asset segregation schemes are more successful when used in the second. Asset purchase and - to a lesser degree - liability guarantee schemes are effective regardless of when they are deployed.

Our analysis of past experiences also suggests that certain tools are particularly effective under specific initial conditions. For example, we find that central bank lending schemes are most helpful when distress follows unusually large asset price corrections of the type triggered by the Covid-19 crisis. Our results confirm that the policy measures adopted since March 2020 have helped to support the economy, including by pre-emptively staving off banking distress.

The rest of this feature is structured as follows. The first section describes how we classify banking distress episodes and, in the process, documents their variety. The second describes the various policy interventions, and includes a box on their variation by time of use and across countries. The third section formally tests the effectiveness of various policy interventions depending on their speed and the type of episode. The fourth section applies these findings to the ongoing Covid-19 crisis. A final section concludes.

Classifying banking distress episodes

Banking distress has various causes and can start from different initial conditions. Such differences must be accounted for when evaluating the effectiveness of policy interventions. Our approach consists of classifying past distress episodes into categories, based on the macro-financial vulnerabilities that preceded them.

We consider 62 past banking distress episodes from Baron et al (2020) for 29 countries over the period 1980-2016.⁵ Baron et al define a distress episode as one in which bank equity prices fall by 30% year on year and there is a higher than normal number of bank failures. Their list differs from others' (eg Laeven and Valencia (2012, 2018)) in two ways. First, it features more episodes, including many distress episodes that did not end in crisis.⁶ Second, the starting dates of the episodes are identified precisely by crashes in bank stock prices.

In order to classify banking distress episodes, we track the evolution of a large set of macro-financial variables in the run-up to these episodes. There are vulnerabilities when (some of) these variables take on abnormal values (Box A). We classify the episodes into five categories, corresponding to five broad vulnerability types: (V1) cross-border exposures; (V2) asset valuations; (V3) bank health; (V4) private non-financial sector (PNFS) leverage; and (V5) real economy performance (Graph 1, Box A and Adler and Boissay (2020)). Vulnerabilities of the first type, for example, typically stem from domestic residents' cross-border liabilities in foreign currencies and cross-border bank loans (Graph 1, left-hand panel). Those related to asset valuations show up as sharp drops in house and stock prices (eg following the bust of an asset price bubble). Vulnerabilities related to real economy performance manifest themselves most often through a prior severe recession, with pronounced falls in manufacturing PMIs and consumption growth.

Our classification highlights the variety of banking distress episodes. The right-hand panel of Graph 1 shows that in about 40% of the episodes, distress is preceded by excessive cross-border exposure, severe asset price corrections⁷ or weak real economy performance. Weak bank performance only precedes 20% of the episodes.⁸

The data indicate some differences between advanced and emerging market economies (EMEs). In advanced economies (AEs), distress episodes tend to be preceded by widespread vulnerabilities, with notably excessive cross-border exposure and weak economic performance (55% of cases). In about 40% of the AE episodes, the initial conditions involve severe asset price corrections or high private sector leverage. In EMEs, it is harder to relate distress episodes to specific vulnerabilities. Just one in four episodes is preceded by excess cross-border exposure or a severe fall in asset prices, and few episodes by excess leverage, whether in the financial or non-financial sector. This suggests that banking distress in EMEs need not be the outcome of domestic imbalances, but could be triggered by external shocks.....⁹

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