An asset-liability management model makes a number of complexcalculations for computing your credit union's interest rate risk.The model's results depend on the assumptions and whether the modelwas set up correctly to handle them. An ALM model summarizesresults in a variety of standardized formats, many of which allowyou to assess the interest rate risk exposure of your credit union.With all the complexities of an ALM model, you want to know that isit incorporating assumptions the way you intended. It's importantto look inside the black box to assess your model's effectiveness.One way to do this is evaluate the model's output forreasonableness. A good place to start would be the fair valuematrix report.

The fair value matrix report lists detailed categories in theleft column followed by columns showing the fair value computationfor the flat rate scenario and each rate shock (up or down 100, 200and 300 basis points). This report, while important, is difficultto analyze. For example, you want to know if the model isincorporating prepayment speeds as intended, and if so, what is theimpact on fair value. Ideally, if you can get this report in aspreadsheet format, you can perform a few calculations that willmake the results easier to analyze.

The first calculation expresses shocked fair value results in aformat similar to that used for bond pricing. For example, a bondtrading at par would have a price of 100 while one trading at adiscount may have a price of 98, and so on. Compute the ratio offlat to shocked fair values for each category under each scenarioand multiply by 100. Let's use a hypothetical portfolio offixed-rate 30-year mortgages. If the fair value of the firstmortgage portfolio under flat rates was $1,200,000, and its fairvalue in a plus-100 basis point rate shock was $1,147,200, thecalculation would be (1,147,200/1,200,000) * 100 = 95.60. Theresults are in the Table 1.

By using a common base, Table 1 allows you to assess interestrate sensitivity for each category at a glance. You can also get anidea if the model's results make sense. For example, you wouldexpect fixed-rate loans to decline in value as rates increase andthe intensity of decline to vary based on the underlyingcharacteristics of the portfolio such as loan maturities, paymentfrequencies, interest rates and prepayment assumptions.

It would also be beneficial to express the impact of rate shocksin terms of its percentage change in fair value for each rate shockcategory. To do this, subtract 100 from values computed in thetable. For example, the +100 column for first-mortgage loans inTable 1 would be computed as 95.60 – 100 = -4.40. So the fair valueof this category of loans would be expected to decline 4.40% ifrates were shocked up 100 basis points. The results are presentedin Table 2.

The format in Table 2 makes it easier to analyze the effect onfair value of different rate shocks for each category. Finally,perform calculations to express the results in Table 2 as the totalpercentage change per 100 basis points. The values for +100 and-100 columns in Table 2 remain the same. The value of the +200column is computed as -10.10/2 = -5.05 and the value of the +300column is computed as -16.17/3 = -5.39. See Table 3.

Table 3 tells more of the story than the previous two tables.What first stands out is that rate sensitivities grow as marketrates increase. In this case, growing sensitivities are due todeclining prepayment speeds as rates rise. Slower prepayment speedsmean it will take longer to receive the full loan principal, whichresults in a lower fair value. The results thus indicate that themodel is factoring in changing loan prepayment speeds as ratesincrease or decrease. It doesn't tell us that the changes inprepayment speeds are reasonable, just that the effects of higheror lower rates on prepayment speeds are considered in thecalculations.

Evaluating rate sensitivities per 100 basis points is an idealway to determine if prepayment speeds and decay rates are beingincorporated in your ALM model as intended and to assess thereasonableness of the results and overall effectiveness of themodel. It allows for a more apples-to-apples comparison betweenrate sensitivities and between categories. More importantly, itallows you to zero in on possible errors that may exist in themodel's set-up, assumptions or data inputs. By looking inside theblack box you can gain a better understanding of the interest raterisk at your credit union and uncover possible errors before theresults are summarized and evaluated against board-approvedlimits. 

Brad Smith is president of ALM Risk Management ConsultingLLC.
Contact 317-200-3150 [email protected]