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Effectively testing for basis risk and yield curve shape risk in interest rate risk analyses

Changes in driver rate relationships are key influences determining the Interest Rate Risk (IRR) position of most institutions. Today, it is commonplace for financial institutions to incorporate testing for basis risk and yield curve shape risk in their IRR analyses. Three elements are needed for a successful basis risk and yield curve risk analysis solution: Asset Liability Management (ALM) model setup and fine tuning; defining the appropriate rate tests; and effectively communicating the institution’s Net Interest Income (NII) IRR position.

ALM Model Setup

ALM model setup is usually straightforward. Basis risk and yield curve shape risk-test scenarios are defined for intake into the model in the same way current forecast type data is. If your ALM model is set up to mute repricing relative to driver rate changes, it may not be necessary to adjust the model’s beta coefficients (which link driver rate changes with new volume pricing). If a fix is required, the easiest fix is to tie core deposit rate paid changes to a short-term rate (e.g. the 3-month Treasury bill) and leave the current betas in place. If core deposit repricing is tied to longer term driver rates, then current betas will need to be modified or eliminated to reflect the fact that longer term rates are moving at only a percentage of the 100bp driver rate change.

Defining Appropriate Rate Tests

Selecting the right basis risk and yield curve shape risk scenarios is a more challenging task. There are inherent risks present in the various types of rate tests. For instance, tests based on forecast economic and monetary conditions alone incorporate basis risk testing and a degree of yield curve shape risk assessment, but the shifting parameters and vendor adjustments may result in sharp variances from period to period. Other rate tests, such as ramps and forecasts, are institution-specific but are not qualitatively constructed and don’t incorporate all the lags and interdependencies found in driver rate relationships.

By remaining aware of test limitations, financial institutions can develop a test regimen that comprehensively projects future rate paths over time incorporating quantified basis risk and yield curve shape risk relationships; provides a stable test environment through time; and has an identifiable and defensible audit trail.

Statistical-based basis risk and curve risk tests are arguably the best solution for assessing basis risk and yields curve shape risk in NII IRR analyses. These tests have several advantages. They are based on advanced statistical analyses of long-term driver rate relationships, firmly anchor the projections in empirically observable data, and create a clear audit trail.

Because the projections are based on equations, any set of rising and declining basis risk tests or steepening and flattening yield curves can be defined and updated every month. The inherent stability of the underlying equations, and monthly updates, ensure that the rate tests evolve over time with changes in interest rates rather than jumping from one period to the next. Further, all statistical-based rate tests have clear and unambiguous audit trails to historic data via the estimation process and equation system.

Effective Communication of the Institution’s NII IRR Position

Multiple tests dealing with basis risk and yield curve risk can cause communication problems because the volume of rate tests and scenario results is daunting, and audiences are easily subject to information overload. How can we effectively present a multi-dimensional view of IRR to all relevant audiences? As with any data presentation, there is no one answer; material provided must be tailored to the audience. The best practice for high-level oversight users of the rate test information, e.g., senior management, board members, and regulators, is to present results in a simple graphic display, with additional detail kept to a minimum. For Asset Liability Committee (ALCO) type audiences, the outputs for each rate test and scenario should be presented, preferably in table format (associated graphics are optional). Model quality assurance and model validation require category-level detail for each rate test by scenario, in whatever is considered the most convenient format.

Conclusion

Conceptualizing the multiple NII IRR rate test outcomes as defining your institution’s IRR surface and presenting rate test outcomes in a three-dimensional format can communicate the multi-faceted face of earnings at risk effectively. A successful basis risk and yield curve risk analysis not only enables better decision making but provides financial modelers with an opportunity to demonstrate to stakeholders the benefits of a more thorough examination of NII IRR.