ALM Basics: Pricing Beta

**Note: This post is part of a series on the basics of asset liability management.

As we have discussed in other posts in this series, the primary way that banks measure short term interest rate risk is via an income simulation:




For short term interest rate risk, banks perform an income simulation. In this simulation, banks make assumptions about how loans and deposits will reprice in a given interest rate environment, and how optionality in the balance sheet (like prepayments and calls) will be exercised. With these assumptions, banks then model what each part of the balance sheet will do in a series of interest rate environments and what the impact will be on net interest income, thereby determining what rate environments represent an exposure to earnings.

As we said in the explanation, banks must make assumptions about how loans and deposits will reprice in a given interest rate environment in order to forecast income through the various scenarios.  Obviously, this assumption is vital to the process, and using an inaccurate assumption can skew the model output 180 degrees in the wrong direction.  For this reason, modelers and examiners pay a lot of attention to this assumption, which is referred to as pricing beta.

In order to estimate beta, banks generally collect their historical offering rates (as far back as they can accurately be gathered) and compare them to market interest rates from the same time period.  The bank must determine how their rates moved historically as market rates moved.  When market rates increased by 1.00%, how did their rates move for each product?  While there are a couple of  ways to frame these relationships (spreads or factors of the market index for instance), the most common way is to calculate the percentage of the movement that was passed on in offering rate.

For example, if market rates increased from 1.00% to 2.00%, and money market account rates increased from 0.25% to 0.75%, then the beta would be 0.5.  This is because the offering rate was increased by 0.5 of the 1.00% increase.  These movements are tracked over time, like this:






The bank will need to use some basic statistics to measure the validity of the correlations, and to determine if there are lags between when the market rate moves and the offering rate is adjusted.  All relationships need to be statistically significant in order to ensure sound model results.

When a valid beta is calculated, the bank can now estimate what its income or expense will be for each account when rates move.





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