Asset encumbrance is a central concept in the context of banks' liquidity crises, as it is associated with their capacity to obtain secured funding. This occasional paper summarises the work carried out by the task force on asset encumbrance, bringing together analyses by the ECB and those national competent authorities working on the topic. First, we describe how asset encumbrance has evolved in euro area banks, focusing on country and business model aggregates. Second, we conduct an econometric analysis of the driving factors of banks' asset encumbrance, highlighting the relevance of credit risk, the availability of high quality collateral suitable for encumbrance, capital and sovereign funding conditions. Third, we turn our focus to the asset encumbrance dynamics of banks that have experienced a crisis. The outcome of this event study analysis indicates that asset encumbrance increases in the lead-up to a crisis, partly to offset early deposit outflows. Building on these findings, we show that asset encumbrance indicators carry predictive information for bank-specific crises as part of a multivariate early warning model.

The paper develops an early warning system to identify banks that could face liquidity crises. To obtain a robust system for measuring banks' liquidity vulnerabilities, we compare the predictive performance of three models -logistic LASSO, random forest and Extreme Gradient Boosting -and of their combination. Using a comprehensive dataset of liquidity crisis events between December 2014 and January 2020, our early warning models' signals are calibrated according to the policymaker's preferences between type I and II errors. Unlike most of the literature, which focuses on default risk and typically proposes a forecast horizon ranging from 4 to 6 quarters, we analyse liquidity risk and we consider a 3-month forecast horizon. The key finding is that combining different estimation procedures improves model performance and yields accurate out-of-sample predictions. The results show that the combined models achieve an extremely low percentage of false negatives, lower than the values usually reported in the literature, while at the same time limiting the number of false positives.

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