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Now that we know what correlations and pricing betas are and how to find them, let’s look into the NCUA call report data to get these statistics for the aggregated data. We looked at five different market rates for this study, which include the 1-Month LIBOR, 3-Month LIBOR, 1-Month Treasury Constant Maturity, 3-Month Treasury Constant Maturity, and 30-Year Fixed Mortgage average.

With these five rates, we also account for a lag in time for each rate with a lag of one month, two months, and three months. One thing to notice is that all of these rates seem to increase between 2004 and 2008 and decrease beyond 2008. Thus, it’s reasonable to expect the correlations and pricing betas to be relatively similar within each deposit rate. Let’s start with regular shares. (For brevity, we will only include plots of the data for regular shares. All other accounts will only have the correlations and pricing betas listed in tables. )

We first calculated the correlations for regular shares with every market rate just listed. All the correlations seem to be high, with the highest being with the 30-year mortgage rate with a lag of one month at 0.96 (highlighted in blue), which is exceptionally high. Thus, the 30-year mortgage rate with a one-month lag will act as our driver rate for regular shares.

Below we have the scatter plot and the time trending plot of the two rates, which reflect what the correlation coefficient is telling us; the two rates move together in a positive and strong fashion.

**2004-2008 2008-2016**

When we fit the simple linear regression models using the 30-year mortgage rate with a one-month lag as our driver rate, we get an up beta of 0.38. So if the 30-year mortgage rate with a one-month lag increases by 100 basis points, we would expect the aggregated regular share rate average to increase by about 38 basis points.

And with the down beta we get 0.37. If the 30-year mortgage rate with a one-month lag decreases by 100 basis points, we would expect the aggregated regular shares rate to decrease by about 37 basis points.

Below are the correlations and driver rates for the remaining deposit accounts.

Below are summary tables of driver rates followed by pricing betas.

In summary, all the deposit rates we explored had extremely high maximum correlations at 0.96. Regular shares and share drafts were most correlated with the 30-year mortgage rate with a one-month lag, while money market shares and share certificates were most correlated with the three-month LIBOR rate with a three-month lag.

One interesting takeaway from the pricing betas is that money market shares and share certificates seem to be a little more sensitive to their driver rates. This isn’t surprising. These deposits typically have higher rates, so people with these deposit types are probably more likely to follow market trends. And this helps explain why money market shares typically have shorter lives.

We also explored correlations and pricing betas at the credit union level. With the credit union level, we went in and calculated the correlations and pricing betas for every active credit union with each market rate with the various lags. Here we present some summaries of what we found.

We’ll look at correlations first. There’s a lot going on in the table above, so let’s take it one column at a time. In the first column, we’re simply looking at the average of the maximum correlations for each credit union for each deposit type. All four deposits on average have a strong positive correlation with their respective driver rates.

The next column shows how many of the maximum correlations were positive. Almost all correlations were positive., so they move in the same direction as their respective driver rates.

The next column shows the most common driver rates for each deposit type. Based on the aggregate level, these results are not surprising. The 30-year mortgage with a one-month lag is correlated strongest with regular shares and share drafts most often, whereas the 3-Month LIBOR with a three-month lag is correlated strongest with money market shares and share certificates most often. The last column just shows the percentage of credit unions whose driver rate matches the one found in the previous column.

For pricing betas, we simply look to the average up betas and average down betas, where each beta is calculated based on each credit union’s respective driver rate.

Again, notice that on average, the betas for the money market shares and share certificates are higher than the others. Also of note, the down betas tend to be higher in magnitude in the downward direction than the up betas in the upward direction.

Moving forward with correlations and pricing betas, a couple of things that would be interesting to examine would be looking at longer periods of increasing driver rate environments. And exploring other market rates could also add some value.

And as correlations/driver rates lend themselves to pricing betas, pricing betas in turn lend themselves to deposit volume modeling. So deposit volume modeling is the next step. Look forward to another sequel in near future!

In conclusion, we’ll reiterate what we said at the start of this journey; it’s all about asset liability management and maintaining that balance. While correlations and pricing betas are great tools for assessing your deposit rates and what to expect with market changes, don’t let sophisticated tools completely negate industry experience. Be sure to combine your experience with these tools to optimize your credit union’s performance.

Product Manager

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