Interest Rate Risk and Asset/Liability Modeling
For the past six years, financial institutions have been lengthening the duration of their loans and investments to slow the shrinking of interest margins. As a result, the NCUA has become more concerned about interest rate risk (NCUA Letter to Credit Unions 14-CU-02) and is focusing more on each credit union’s method of measuring and addressing IRR.
Other regulatory agencies have raised IRR concerns as well. The recent run-up in interest rates has further stoked IRR concerns among CEOs and regulators and has sharpened the debate among these parties as to the efficacy of different approaches to asset/liability management modeling. That debate revolves around key issues such as:
- The assumptions (quantity and quality) that are being used as a basis for a financial institution’s ALM modeling process;
- The depth of understanding of ALM modeling concepts on the part of managers, boards and examiners;
- Using past experiences as a basis for assumptions fed into an ALM model;
- The efficacy of traditional ALM modeling processes (particularly net equity value);
- The lack of proper consideration for operational expenses and non-interest income in some ALM models;
- The accuracy and fairness of examiners’ criticism of ALM modeling outcomes used by managers who are trying to balance profitability with safety.
Clearly, in volatile times like now, a financial institution’s future could well depend on the ALM model it uses, the accuracy of that ALM model’s conclusions, and how management uses those conclusions in its planning and forecasting. Let’s compare two distinct approaches to ALM modeling:
The Net Equity Value ALM Model
The traditional method for measuring IRR is an ALM modeling tool that relies on estimating the net equity value of a financial institution’s balance sheet. Some students of ALM point out that there are inherent weaknesses in NEV that need to be taken into consideration by managers and regulators. These weaknesses include:
- The level of dependency on assumptions to estimate the maturity of non-maturity deposits;
- Using discounted cash flows to arrive at the present value of a financial institution’s balance sheet (a financial institution’s liquidation value);
- The assumption that discounted cash flows of a balance sheet can be used to estimate changes in net worth and earnings resulting from changes in market rates;
- The assumption that a financial institution’s loan portfolio is equivalent to bond portfolios that are widely traded;
- The assumption that a financial institution’s deposit accounts can be treated like bonds which have contractual maturities and are widely traded;
- Small inaccuracies in the assumptions used that could lead to erroneous ALM conclusions that can in turn result in perilous management decisions.
Next Page: Earnings at Risk
The Earnings at Risk ALM Model
Arguably, a more accurate and easier to understand ALM model uses earnings at risk to measure a financial institution’s IRR. EAR does not rely on assumptions to the extent that NEV does and therefore has greater value for CEOs and CFOs who are trying to forecast the effects potential changes in interest rates will have on profitability.
The best EAR models project cash flows and impacts on profitability using actual payments coming from individual loans and investments in a financial institution’s balance sheet. Better-quality EAR models also take into account additional factors that affect profitability such as fee income, maturing CDs and operating expenses.
EAR ALM models assure validity by holding constant the assets and liabilities in a balance sheet so as to measure the actual IRR in the current balance sheet. Once the base IRR is established, an EAR model can also be used for multiple simulations where management can vary inputs and view the impacts each change or combination of changes has on IRR, income and equity. Simulations may include (1) increasing or decreasing loans and/or investments; in combination with (2) increasing or decreasing deposits (specific types or general) including changing the mix of deposits; and (3) changes in operating expenses including provisions for loan losses.
ALM models should include the effects of interest rate shocks that measure the impact possible changes in interest rates will have on balance sheets and profitability. Better models measure the impact of two distinct possible rate shock scenarios: (1) an immediate, extreme (typically 300 to 600 basis points) increase or decrease in rates; and (2) small but continued changes in interest rates (sometimes referred to as stepped shocks).
Many EAR models that are based on careful research use price elasticity of demand applications to determine the percent of shock that can be applied to each deposit type to maintain current balances of deposits. This price elasticity measure then employs a regression model to predict runoff at distinct interest rate increases. This algorithm is used to determine the magnitude of cost of funds increases required to fund loans and investments to maturity (based on the current balance sheet configuration).
A financial institution’s ability to mitigate the effect of interest rate shocks generally rests in its ability to re-price its loan/investment portfolios. Stochastically derived EAR models usually set reductions in loan/investment portfolio principal balances in repayment schedules typically within a set range in maturities and with even cash flows. Therefore, a carefully designed EAR model can use the weighted average maturity of loans/investments to create an anticipated amortization schedule for principal balances of loans/investments.
Loans/investments with common amortization patterns (as determined by WAM) are input into the model. Using the WAM, a straight-line calculation of amortization is derived. The amount of amortization is then applied to each quarter and year as a baseline for re-pricing. A statistically derived algorithm is then applied to the amortization schedule in shock scenarios to allow for anticipated changes in prepayment speeds resulting from shocks of increasing magnitudes. This allows managers to choose different shocks in simulations to test the limits of IRR in their balance sheets.
In summary, it can be argued that a stochastically derived ALM model utilizing EAR can provide a more meaningful IRR management tool for financial institutions than NEV. Furthermore, arguments are made that EAR provides more accurate planning applications – a definite advantage over NEV models which often rely on many unsubstantiated assumptions. Considering the advantages EAR appears to have over the more traditional NEV, financial institutions would be wise to give the EAR approach to ALM careful consideration.