Mitigating Risk & Strengthening Model Framework with Good Governance

It has already been established that having a solid understanding of Interest Rate Risk (IRR) and a sound IRR modeling process is critical to supporting management’s deployment of balance sheet strategies for financial Institutions. But a sound IRR model is not enough. Ensuring solid governance, controls, and policies surrounding your risk model bolsters the reliability and effectiveness of the whole operation. In fact, regulatory guidance emphasizes the role of effective model governance in minimizing ongoing model risk.

What Constitutes a Solid IRR Policy?

A sound IRR policy encompasses every important element of the overall modeling process. It establishes accountability and clarifies responsibilities while addressing each aspect of Asset Liability Management (ALM) modeling, from systems to measurement methodologies and risk analyses.

The IRR Policy should also take stock of the policy limits and expectations and include monitoring and risk-mitigating strategies. Lastly, it must keep track of approvals, validation, and policy interrelation. In short, the IRR policy’s job is to carry out the full disclosure of the entire “Implicit Contract” between all stakeholders for Interest Rate Risk management.

This first step is to establish clear and effective governance. Include in the ALM Policy a statement to affirm the Board of Directors (Board) acceptance of the full responsibility for risk of the Institution, as well as delegating the day-to-day management to the Asset Liability Management Committee (ALCO) and to management. This statement must communicate this intent. Also, include a comprehensive list of responsibilities and duties for the Board, ALCO, and Management in order to establish clear lines of authority for all aspects of IRR management.

Second, policy should outline the Institution’s IRR management philosophy and procedures for the employment of comprehensive systems and standards for measuring and monitoring IRR, valuing positions, and assessing performance. This includes the IRR measurement software used, the IRR monitoring methods (NII IRR and EVE / NEV) and frequency, the process employed for the balance sheet (i.e., static), base rate projection (i.e., spot rates held constant, and the time horizon measured). It is noted that regulatory guidance mandates earnings at risk be assessed over a minimum of 24 months.

The Policy should also define the IRR scenarios that are modeled to assess risk such as parallel and instantaneous rate shocks (e.g., +/- 400 bp in 100 bp increments). Policy should also define alternative scenarios that are modeled. The Advisory on Interest Rate Risk dated January 6, 2010, mandates the rate of interest rate scenarios be sufficient to identify basis risk, yield curve risk, and risks of embedded options. This can be done by fully implementing a comprehensive IRR modeling program.

Defining Exposure Limits

An important part of Policy is defining exposure limits for risk. Policy limits or guidelines represent the Board’s “appetite for risk” – that is, the level of risk an organization is willing to take. One set of limits that suits one Institution will not apply to another, as noted in the previous blog article. The inherent risk in an Institution’s balance sheet is primarily dictated by the current product structure. Limits should be defined by rate scenarios for both earnings at risk and EVE / NEV. Standard practice is to define rate shocks (instantaneous and parallel). Include a notation to allow the ALCO to suspend particular rate scenarios (i.e., -300 or -400 bp) due to market conditions and rate environments. You do not want to measure a rate scenario that is not capable of shocking down the full amount at all points on the curve.

NII IRR measurement captures the short-term impact of interest rates changes on balance sheet performance. Limits need to be defined for all rate scenarios measured by the Institution for both the 12-month and 24-month timeframes. Generally, limits should expand linearly at a minimum and be greater for year 2.

Economic Value of Equity (Net Economic Value of Equity) IRR measurement captures the long-term impact of interest rate changes on the market value of the balance sheet. Similar to the limits for NII IRR, limits should expand linearly at a minimum. Policy guidelines or limits for EVE / NEV are typically larger than NII limits due to the long-term nature of the measurement. The amount of optionality contained in the balance sheet will factor strongly into the Institution’s IRR limits. A balance sheet with a material amount of optionality will have limits that expand more (non-linearly) due to the potential of triggered options (i.e., calls or puts). Options change the maturity and repricing of cash flows as interest rates rise or fall.

Include in the Policy, a discussion regarding risk mitigation strategies. This will meet regulatory guidance mandates to list potential actions to bring limits back to acceptable levels. Ensure a statement that notes actions are not limited to only the strategies listed in the policy.

One key step that is often overlooked in IRR policies is to define actions that should occur when there are exceptions. Define the actions that are required when policy exceptions occur such as informing the ALCO and Board, requiring additional what-if scenarios, more frequent monitoring and testing, and present strategies for correction timing of IRR.

Model User Control & Documentation

Model control and user documentation is the process that wraps around the model. These key areas are often overlooked or underappreciated for the importance that they serve the Institution. Good model control documents provide transparency of the ALM process and help instill confidence in the user with the Board and ALCO. Documentation can also preserve and establish the “corporate memory” of the model changes and processes. This is especially key when the person who set up the IRR model leaves the Institution.

Whether an outsourced or in-house model, controls and documentation need to include the following.

1. User Checklist, to formalize and document completion of each key modeling step.
2. Change Control Log to serve as a corporate memory of all notable developments and changes to setup, data, inputs & assumptions.
3. Comprehensive Model Process Documentation that details all modeling processes and procedures. This document is ideally one single document with process flow charts, screenshots, and specific directions for all functions from data gathering to reporting. This should include all assumption derivation methodologies, and sources, to establish both the how and the why of key assumptions.
4. Incorporate Model User Access, Model Admin, and Back-Up considerations, such as a model contingency plan.

A strong model control environment provides process transparency and improves management “buy-in” by increasing confidence in model results. That confidence enables management to make strategic balance sheet decisions based on model results.

Ongoing Model Monitoring Plan

To proactively verify the organization’s continued compliance with regulatory requirements, on-going model monitoring (OMM) is a critical tool for risk management processes. An OMM plan helps ensure that your IRR model is appropriately implemented and performing as intended. The plan should detail the Institution’s model performance testing to confirm accuracy and limitations.

The OMM plan consists of four primary components.

1. Outcome’s analysis or back testing. This process simply compares the results of a model forecast compared to actual results to confirm accuracy.
2. Back testing of primary assumptions is also part of this process to confirm reasonableness or to determine the need to recalibrate.
3. Benchmarking. This process can include comparing the last model results to the current model or actuals to forecast to provide a benchmark for reasonable results.
4. Sensitivity and Stress Testing Assumptions. The goal is to quantify the risk from primary assumptions through testing alternatives, “what-ifs”. Sensitivity and stress testing are what-if scenarios that quantify the impact of a change in an assumption from a moderate (sensitive) and more severe (stress) outcome.

The Bottom Line

Establishing a strong and sound model governance solution is the most effective strategy to ensure ongoing model risk is minimized, attested to by regulatory guidance. Along with establishing a sound IRR policy, effective user controls, and developing comprehensive model documentation, Institutions must put implementing a hands-on ongoing monitoring plan at the forefront of their IRR processes. Download our full guide on how good governance strengthens your model framework and mitigates risk here.

Written by Chris Mills, Senior Director, and Madonna Ritter, Senior Vice President