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Summary of "Euro area banks’ interest rate risk exposure to level, slope and curvature swings in the yield curve"

Core Content

This document presents a study on the interest rate risk exposure of major euro area banks (SSM banks) to different components of the yield curve, namely level, slope, and curvature swings. The research utilizes a Bayesian Dynamic Conditional Correlation multivariate GARCH (DCC M-GARCH) model to measure time-varying sensitivities and explores the factors influencing these exposures.

Main Points

• Interest Rate Risk as a Major Risk Source: Interest rate risk is identified as a significant risk factor for financial institutions, particularly in the context of the euro area's low interest rate environment.

• Yield Curve Components: The yield curve is decomposed into three components: level, slope, and curvature, each representing different types of interest rate risk.

• Model Used: The study employs a Bayesian DCC M-GARCH model to estimate the conditional variance-covariance matrices for each bank, which allows for time-varying sensitivities. This model is an extension of the standard DCC M-GARCH model and incorporates asymmetric error distributions.

• Exposure to Yield Curve Changes: SSM banks exhibit a positive exposure to all three components of the yield curve. Share prices increase when the yield curve level, slope, or curvature increases. This suggests that banks benefit from upward movements in all these components.

• Time-Varying Sensitivities: The sensitivity to yield curve changes varies over time. For example, the sensitivity to level changes increased significantly from 2008 onwards, while slope and curvature sensitivity became positive from 2010 onwards.

• Bank-Specific Factors Influencing Exposure:

  • Balance Sheet Composition: Banks with a higher proportion of net customer loans to total assets show higher exposure to all yield curve components. Conversely, higher capital ratios and a lower proportion of deposits reduce exposure to slope changes.
  • Profitability: Net interest income to operating revenue and ROAA (Return on Assets After Adjustment) have mixed impacts on interest rate risk exposure. Net fee income to risk-weighted assets has a negative impact on curvature exposure.
  • Asset Quality: Loan loss reserves to gross customer loans have a negative impact on curvature exposure.
  • Size: Larger banks (measured by total assets) tend to have higher exposure to interest rate risk.

Key Information

• Sample: 36 listed SSM banks in the euro area, with data covering the period from 2005 to 2014.

• Time Period: Daily data from January 2005 to December 2014.

• Data Sources: ECB and Datastream.

• Methodology:

  • Sensitivity Estimation: Using the formula:
    $$
    \widehat {\boldsymbol {\beta} _ {I R , t} ^ {(i)}} = \frac {C o v (\boldsymbol {r} _ {i t} \Delta I R _ {t})}{V a r (\Delta I R _ {t})}
    $$
  • Linear Model:
    $$
    \widehat {\boldsymbol {\beta} _ {I R , t} ^ {(i)}} = \boldsymbol {X} _ {i t} ^ {T} \boldsymbol {b} + \boldsymbol {Y} _ {i t} ^ {T} \boldsymbol {\theta} + \varepsilon_ {i t}
    $$
    Where $X_{it}$ represents bank-specific characteristics and $Y_{it}$ represents year- and country-fixed effects.

• Key Findings:

  • All SSM banks show positive exposure to yield curve level changes.
  • Most banks (35 out of 36) show positive exposure to slope changes, with one exception.
  • Most banks (31 out of 36) show positive exposure to curvature changes, with five exceptions.
  • Larger banks, with higher capital ratios, a higher proportion of customer loans, and lower deposits, are more sensitive to interest rate risk.

Conclusion

The study concludes that interest rate risk exposure for SSM banks varies over time and is influenced by both the yield curve components and the banks' balance sheet and profitability characteristics. A dynamic model is necessary to capture these time-varying sensitivities. Additionally, curvature swings account for a significant portion of the yield curve variation, highlighting their importance in assessing interest rate risk.