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To quantify this IRR exposure, management would need to ensure that the ALM model is capable of evaluating changes to more than one key market rate. Another example that has become more prominent in recent years is a bank that originates and sells mortgage loans but retains the servicing rights. Some ALM models only measure changes to net interest income NII rather than potential changes to all income and expense categories.

Since fee income from mortgage originations and ongoing servicing fees are sensitive to interest rates, calculating the change in NII would fail to capture the fee income at risk in various rate environments. Banks with significant noninterest income that is sensitive to changing rates should focus special attention on quantifying potential changes to net income.

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A bank should ensure that its ALM model is capable of quantifying the effect that market rate variations could indirectly have on its earnings. More broadly, a bank should also understand the benefits and limitations in the level of detail for which assets and liabilities are analyzed in the model. A model that is based upon Call Report schedules may be appropriate for lower-risk banks with homogeneous loan and security characteristics. While these ALM models are often less expensive and more easily implemented and operated, grouping assets and liabilities in the model based upon Call Report categorization also has a downside.

For example, Call Report instructions define any loan operating at or below an interest rate floor as a fixed-rate loan. ALM models using this categorization of assets would also treat these otherwise variable-rate loans as fixed-rate loans and miscalculate the contribution of these assets to earnings in various interest rate change scenarios. Call Report-based models have similar limitations for other loan and deposit features as well, lessening their accuracy as a risk measurement tool.

An effective IRR measurement tool is expected to have an appropriate degree of precision, which depends upon properly established assumptions. While regulators do not expect an ALM model to predict the future, the data used in the tool should have a high degree of accuracy. If data inputs or model assumptions are invalid or inaccurate, the model output reports will not be very useful and could result in poor decisions being made.

Likewise, if the reports do not provide meaningful information, they could be ignored by management. As community bank examiners have reviewed ALM models over the past 15 years, they have found that two common assumptions significantly impact the accuracy of model results - deposit behaviors and prepayments.

Interest Rate Risk

In fact, slight errors in these assumptions can result in significant errors in ALM model results. Assume, for example, that prevailing interest rates increase from 1 percent to 2 percent, and management increases the rate paid on savings accounts from 0. The beta is then 0. ALM models use the average life of an NMD balance as their effective maturity when projecting cash flows. Deposit products continue to represent the most significant funding source for community banks, making deposit assumptions critical to ALM model accuracy.

While a bank holds the option to set deposit rates for NMDs and other deposit products like CDs, consumers hold the option to withdraw funds at will. Consequently, assumptions like deposit betas and deposit average lives play a vital role in a bank's measurement system. See box above for descriptions of deposit beta and deposit average life. Most ALM models provide a bank with the flexibility to customize deposit betas.

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However, not all ALM models provide the ability to input different deposit betas for rising and falling rate scenarios. Deposit betas indirectly affect projected interest expenses under various interest rate change scenarios. In most situations, banks delay raising or lowering deposit rates at the beginning of a rate cycle.

When a bank finally elects to change deposit rates, it often will do so to a lesser extent than the prevailing change in market interest rates and often to different degrees depending on whether the rate change is upward or downward. Thus, setting deposit beta assumptions is challenging, as bankers must balance controlling interest expense with customers' ability to transfer accounts. An ALM model's deposit assumptions also include setting deposit average lives. Risk managers should explore how an ALM model enables deposit average life information sometimes entered as a rate of decay to the balance to be input.

Many community banks turn to vendor-supplied deposit assumptions as a starting point or source for setting the average life for NMD products. Bank management should evaluate how any vendor-supplied assumptions in the model, such as deposit decay rate tables, are updated and maintained by the vendor and compare them with their customers' behavior.

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In today's environment, deposit volumes at community banks are at high levels relative to total liabilities. Many community banks have also experienced migration of customer balances from CDs into NMDs since Sensitivity testing takes one key assumption, such as deposit betas, and changes the value to be larger or smaller than its current value. The model scenarios are then run again to see what impact changing one assumption has on the overall ALM model results.

Typically, one of the most difficult IRR measurement challenges is modeling cash flows for mortgages and mortgage-related products. For example, the uncertainty of expected cash flow timing and amounts for products such as residential mortgages, mortgage-backed securities MBS , and collateralized mortgage obligations CMOs depends on the embedded option held by each underlying borrower to refinance or prepay.

Conversely, during periods of increasing rates, this incentive diminishes and prepayments are likely to be lower. Volatile mortgage refinancing cycles over the past decade, however, have not followed traditional theory, which further emphasizes the difficulty in developing prepayment assumptions. As illustrated, homeowners' refinancing activities have not always behaved as expected during periods of interest rate changes, which causes difficulties in estimating future cash flows and potentially leads to erroneous IRR model results.

There are a number of standard calculations for measuring the impact of changing interest rates on a portfolio consisting of various assets and liabilities. The most common techniques include:.

Measuring Interest Rate Risk

The assessment of interest rate risk is a very large topic at banks, thrifts, saving and loans, credit unions, and other finance companies, and among their regulators. Much of what is known about assessing interest rate risk has been developed by the interaction of financial institutions with their regulators since the s. When a bank receives a bad CAMELS rating equity holders, bond holders and creditors are at risk of loss, senior managers can lose their jobs and the firms are put on the FDIC problem bank list.

See the S ensitivity section of the CAMELS rating system for a substantial list of links to documents and examiner manuals, issued by financial regulators, that cover many issues in the analysis of interest rate risk. The assessment of interest rate risk is typically informed by some type of stress testing.

Interest Rate Risk Management - FinSer

See: Stress test financial , List of bank stress tests , List of systemically important banks. From Wikipedia, the free encyclopedia. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. This second edition builds on the previous edition by covering new applications in counterparty credit risk management scenarios. The new edition also features an advanced study on the market price of risk, and gives some additional numerical examples.

The book is a valuable reference for finance managers, risk managers, and economists involved in industry levels interest rate modeling.