ECB: Understanding the U-Shaped Relationship Between Collateral and Default Risk
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Reserve Bank of India (RBI)
The ECB working paper titled ‘Risky collateral and default probability’ utilizes granular data from the AnaCredit database to challenge traditional assumptions regarding the relationship between collateral and borrower default. While collateral is typically viewed as a safety net, the study finds that the correlation between the collateral-to-loan ratio and the Probability of Default (PD) is non-linear and follows a distinct U-shaped pattern.
Key findings from the Eurozone-wide analysis include:
The Riskiness of Collateral: Higher collateralization often signals that the underlying asset itself is volatile. An increase in the ex-ante collateral-to-loan ratio correlates with greater variance in the asset’s market value after loan origination.
Moral Hazard and Effort: Excessive collateralization can diminish a borrower’s incentive to exert effort to prevent default. When a borrower pledges highly risky collateral, their “skin in the game” effectively decreases if the asset value plummets, leading to higher default rates.
Informational Advantage: The study reveals that the common empirical finding of “riskier borrowers pledge more collateral” is often a result of banks having superior information compared to external economists. When accounting for time-varying bank and firm-specific risk factors, the theoretical negative correlation holds—but only up to a point.
Sectoral Sensitivity: The impact of collateral volatility is most pronounced in sectors with fluctuating asset prices, such as real estate and heavy manufacturing.
What is the ‘U-shaped relationship’ in credit risk? It describes a phenomenon where, initially, adding collateral reduces a bank’s risk because it provides a buffer against loss. However, once a loan becomes “overcollateralized” with risky assets, the probability of default starts to rise again. This happens because lenders demand more collateral to compensate for the asset’s own riskiness, which in turn reduces the borrower’s net benefit from the project, leading to a “moral hazard” where the borrower is less motivated to ensure the project’s success.
Policy Relevance
For India’s banking sector, which is heavily reliant on physical collateral like land and machinery, these insights are critical for refining the Internal Ratings Based (IRB) approach under Basel III norms.
Strategic Impact for India:
Refining Credit Models: Indian banks and NBFCs can utilize these findings to move beyond static collateral valuations. Integrating collateral volatility into credit risk models can prevent the “over-leveraging” of risky assets in sectors like Real Estate and Construction.
Macro-prudential Oversight: The RBI can consider developing risk-weighting frameworks that account for collateral variability, especially during economic stress periods (like COVID-19) where asset prices in India fluctuate significantly. Regulators can use these findings to guide banks in adjusting risk-weights for loans backed by highly volatile sectors like Real Estate and Construction.
Data Harmonization: The study emphasizes the need for granular, centralized credit data. Strengthening platforms like the Central Repository of Information on Large Credits (CRILC) with collateral-specific metrics would allow for better systemic risk assessment.
SME Lending: For MSMEs, where collateral is often the primary entry barrier, policy interventions could focus on “quality” of collateral rather than “quantity,” reducing the moral hazard risks for small-scale borrowers.
Follow the full paper here: Risky collateral and default probability