​Case Study: Deposit Valuation Analysis - Using FHLBank Boston Advances to Efficiently Price Deposits

Case Study: Deposit Valuation Analysis - Using FHLBank Boston Advances to Efficiently Price Deposits

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Hey everybody. Welcome to part two of our case study around deposit valuations and how to leverage that information to make better funding decisions.
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Once again, I'm Sean Carraher, and I joined the Federal Home Loan Bank of Boston just a couple of months ago in May of 2022.
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I have over 20 years of experience in the industry, and I'm well versed in asset liability management.
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I've been the treasurer of two different multi-billion dollar banks in the immediate Boston area and have chaired ALCO groups, created and run funding and derivatives strategies, and created and managed profitability and risk frameworks.
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At the end of the first part of this case study around deposit valuation, we recognize the fact that we already have ALM metrics that are associated with the economic factors that create value for deposits.
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And, we can leverage those metrics to create a framework that will put a number against our deposits to actually use in different kinds of quantitative analysis.
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And so, very briefly, again, those economic attributes are the degree to which pricing will adjust, which is the beta.
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The speed with which pricing is going to adjust, which is the lag.
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The volatility of our balances, which is the decay rate.
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And the amount of time over which the deposit is expected to remain outstanding. And that's the average life.
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And the fact that our deposits have an opportunity benefit instead of an opportunity cost relative to other sources of funding because we'll be able to use those client relationships in other ways.
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So, how are we going to do this? How are we going to go about creating that framework that combines all those attributes?
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Well, first, we're going to take one of those pieces of intuition that we had in the first part of the case study and recognize the fact that not all accounts and not all deposit products behave the same.
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And so, we can bucket balances within a deposit product by a combination of the repricing behavior of that product and the volatility of that product.
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So, some balances are going to be really price sensitive, and some balances are not going to be price sensitive.
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Some accounts are going to be more volatile, and some accounts are going to be less volatile.
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We can use that information to start to create buckets of dollars, and assign … a percentage of that overall product to that bucket.
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Then we can look at each one of those buckets and create a term, that is a period of time, that's associated with that behavior.
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So, [if] something is not very price sensitive and it's not very volatile,
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then it probably has a very long life.
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And if something is really price sensitive or if it is really volatile, then it has a very short life. Then finally, we're going to add a premium to each one of these buckets to recognize the fact that, irrespective of the economic attributes of the bucket,
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it has an opportunity benefit relative to other sources of funding.
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A lynchpin in creating the framework for this deposit valuation methodology is the recognition of a hidden mathematical relationship that exists when we identify deposit betas.
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While we normally think of using deposit betas as being applied to all the balances in a product type, mathematically, we can theoretically divide the balances within a product type into two buckets:
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one bucket that is 100% price sensitive and one bucket that is 100%
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not sensitive, and the size of each one of those buckets is the beta.
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So, for example, if we had a money market account that we assumed had a 60% deposit beta, what we're actually mathematically saying is that 60% of the balances have 100% price sensitivity.
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And 40% of the balances have a 0% price sensitivity.
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And so, when you put those two buckets together, you create an overall sensitivity of 60%.
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But we can differentiate that into 2 pieces.
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One piece is really fully sensitive, and one piece is really not at all sensitive.
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Now, in reality, not every account is going to behave that way, not every account is fully sensitive or insensitive.
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But theoretically mathematically, we can create these two different buckets that sort of create a framework in which we can now start to think about differentiating different types of accounts.
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Deposit betas allowed us to break an undifferentiated mass of balances in a product type into two discrete buckets.
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If we introduce the concept of volatility, we can now start to break it into three different pockets, and if we think about what volatile balances would be, first off, if we remember from our first slide upfront, that every attribute, every economic attribute, has an associated ALM metric,
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we can recognize that the decay rate that we're using to build our EVE or any EVE modeling, it's essentially the volatility estimate, and we can also then take a step back and say OK, if a balance is volatile, how do I put that in the framework of deposit betas?
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Is it price sensitive or is it price insensitive?
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And I suspect you'd agree that a volatile balance is sort of, by definition, highly sensitive.
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And so, therefore, a volatile portion of our balances is going to come out of the price sensitive bucket.
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So, an example that's on the slide, we said, OK, there was going to be a 60% deposit beta, so 40% of our balances are price insensitive,
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and 60% were going to be price sensitive.
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Now, what if we assume that we have a 10% decay rate which is effectively asserting whether it's going to have a 10% average life.
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If there's a 10% decay rate on the product type, then 10% of the overall balance is both sensitive and volatile and now 50%, the 60% upfront minus the 10% volatility is now sensitive, but not volatile.
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That is, these balances are price sensitive, but they're not here today, gone tomorrow.
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The volatile balances are here today, gone tomorrow, and the non-volatile, price-insensitive balances, are going to stick around indefinitely.
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So, we've created three buckets just from two economic attributes: the beta and the volatility.
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And the volatility is the decay rate.
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We've created these three buckets based upon the price sensitivity and relative volatility of the balances within a product type.
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And so that insensitive non-volatile bucket,
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we can call that core fixed. It's funding that's not going to move around its core funding, and it's not price sensitive.
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The highly sensitive, highly volatile bucket, we can call non-core float because these balances are not core balances.
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They're balances that could be in flux and they're, they're price-sensitive balances.
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And then the in-between bucket are those balances that are core funding, they'll stick around but they'll stick around if we pan.
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And so that's sort of the in-between hybrid core-float piece.
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Now we set upfront in the framework that not only do we have to identify the different kinds of buckets or the size of the buckets within this framework based upon repricing and volatility characteristics, we have to put a term against each one of these.
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Remember, if we're going to value a bond, we need to know the cash flows, and we need to know how long those cash flows are going to last.
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Well, within this framework, if we have a piece of the puzzle, that's this core fixed bucket that isn't price sensitive, and isn't very volatile, it's not responding to price inputs and its behavior. It's only going to respond to life events, so to speak, within its behavior.
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So, the term of it is the average life. It's got a very long life associated with it.
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The non-core float, the really volatile price-sensitive piece, is assumed to have only an overnight value because those are balances that are here today and gone tomorrow.
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And then the in-between piece, the core float has a life that's the, that's the same as our lag term that we're using it and I'm modeling, that is to say those balances will stay so long as we pay him within some timeframe.
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And that timeframe doesn't have to be today or tomorrow.
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It could be two months, three months, six months, a year, but we're going to have to pay them to keep them happy.
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Upfront we identified that there were five economic attributes that created value for deposits, and we've used four of them.
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We figured out the price sensitivity, we figured out how quickly prices will change, we figured out volatility in average life, and we use the deposit beta to figure out the initial buckets and then we add in volatility to identify three different buckets. Then we tried to figure out the term by
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looking at average life and the lag terms.
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The last piece of the puzzle is the economic benefit.
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The opportunity benefit, having client deposits relative to wholesale funding in the first place.
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So, I think it's clear that client deposits do have value over wholesale funding sources.
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Typically, they're not collateralized.
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You can facilitate client development by using deposits, and they can reduce your capital and liquidity, liquidity requirements through regulatory perception.
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So how do we figure out the value of those deposits relative to wholesale funding sources?
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And this concept of term liquidity premium can accomplish that, and very simply, it's just the marginal cost of borrowing relative to the swap rate associated with the long term.
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So, an example that's on the slide and left-hand side.
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If we know that a 10-year advance is 3.71%, and a tenure swap is 2.62%, the liquidity premium’s just the difference in those two things, which is 1.09%.
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That is to say, the marginal cost of locking in your liquidity of funding for your balance sheet is 109 basis points relative to just taking care of the interest-rate risk in a swap.
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​So, that represents the value of having client liquidity versus wholesale funding.
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Let's walk through a couple of different examples about using this framework to value different types of accounts.
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So, on this slide, we're taking a look at an average sensitivity NOW account.
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And the key assumptions around that account, which could be taken or derived from the ALM modeling that you are already accomplishing,
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is identified in the light green up on the right-hand side.
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So, the rate paid on this account is 25 basis points.
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So, NOW account. So, it's got some beta, but it's not very high, 30%.
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The lag pricing assumption is that it won't reprice for six months.
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The decay rate’s 8%.
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It's got some fee revenue and servicing costs, and deposit insurance associated with it.
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And the deposit life in this tool is just assumed to be the inverse, the reciprocal of the decay rate.
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That's just a mathematical truism.
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So, given those assumption inputs, if you recall how we put together the framework, we know with a 30% beta that means there is a 70% core-fixed allocation.
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That is to say 70% of the balances in this account type are assumed to be not volatile and not price sensitive,
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while 30% are assumed to be some combination of volatile and price sensitive.
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And so, you see here that the non-core float is the same as the decay rate, 8%.
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So that's the percentage of balances that are assumed to be volatile
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because the decay rate is the amount of balances that are assumed to be gone within a year.
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And then the core float piece, the piece, the balances that are price sensitive, but not particularly volatile,
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that's 22% of the overall pie because if we pay those balances, they'll stick around.
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And then we know the term associated with each one of those things.
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The core fixed has a very long life, and that's this deposit life number of 12.5.
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And so, we look to the swap curve to figure out what's a 12.5-year swap worth. And it's worth 266 in this example.
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The core float piece is basically, the pricing lag.
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And so, what would be a six-month rate. In this example, it's 284.
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And, again, those numbers may seem a little odd right now, but that's because the yield curve right now is inverted.
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Then, finally, the non-core float just gets an overnight rate, and as I record this, that's at about 165. And the Fed is widely assumed to be repricing in the near future up to a level of 250 bucks.
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Right now, it's at 165.
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We also figure out the GOP's associated with each one of those terms, and then add it all up.13:51 And we get to a gross profitability of 351.
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Put in the servicing costs, and the deposit insurance and the fee revenue associated with the account,
13:58 and the profitability, net of all those revenues and costs, is 3.26.
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And so, when we start to think about the analysis that we're going to do for this type of account, even though this NOW account looks like it has expense associated with it, a 25 basis points from an accounting perspective, economically, it has profitability of 3.26%.
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It probably has better profitability than most lending products that depositories offer.
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Let's turn our attention to another example of an average sensitivity MMDA.
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Here, we've got a 60% beta. We're paying more at 80 basis points. It's got a higher decay rate.
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And it's got a shorter pricing lag because it's a more sensitive type of balance, and we'll see that that adjusts how much of the overall balances are accorded to each one of the theoretical buckets.
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And then we apply the same type of logic and math to it,
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and we get to an overall profitability of 2.22%.
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Recall that the NOW example had a profitability of over 3%.
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And so, this average sensitivity MMDA has significantly less value than the NOW account.
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But, again, that lines up with our intuition.
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If the NOW account has much lower sensitivity, and it's likely to stick around for a lot longer, it should be worth a lot more than the money market account.
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Finally, let's take a look at the high cost, high sensitivity MMDA.
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And so, this would be the type of product that might be introduced in an environment like this, where rates are rising quite rapidly
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and deposit volumes might be getting squeezed,
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and a depository might be saying, how do we fund the balance sheet most appropriately?
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Should we introduce a deposit special that has a really high rate?
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And so, if we apply that same logic to this potential product, we can figure out what's the value of that product.
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And in this example, we said, OK, what if the rate paid on this account is 1.5%?
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It has a 95% beta. It's going to be really sensitive balances, and it's going to have no pricing lag because we're going to have to pay up in order to keep these balances around right
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now. It's got a much higher decay rate, which means it's going to have a shorter life.16:10 How does this stack up versus the other products?
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We would all assume intuitively this has much lower value than the other products from our core clients.
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But if we apply the framework to this, we can actually put numbers around all of it.
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And, again, we put those buckets together, we figure it all out, and we get to a profitability here of 66 basis points.
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And so, the pricing in this example is only 70 basis points higher than a typical MMDA that we had on the prior slide, but the profitability is 156 basis points lower.
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Again, the accounting doesn't necessarily line up with the economic reality of the situation.
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If we perform this type of analysis on all the different types of products that are on the balance sheet, we can figure out the profitability of the overall non-maturity deposit book.
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So, in the examples building off the slides we had from the prior few slides, we can figure out the value of DDAs,
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NOW, savings, MMDAs based upon the modeling assumptions we're using for each one of those product types.
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And in that case, we would come to an overall non-maturity deposit profitability of about 282.
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That's to say that the overall value of the non-maturity book economically is 2.82%.
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And then, building further on that, if we introduce a high-cost product because we need to find marginal funding,
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it's a likely scenario that some balances in the existing book are going to migrate and be cannibalized by that high-cost money market account.
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And if we remix the overall balance sheet by some percentage, then the overall profitability of the book is going to go down because the high-cost money market is really expensive. It doesn't have to be a money market, it could be a savings account, could be any type of account.
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But those different accounts are going to cannibalize some of the business of our existing book.
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And so even though dollars are raised, it's actually economically eroding the value of the deposit franchise.
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And now we can come to a place where we can evaluate the benefit of taking an advance, or not, relative to introducing a deposit special.
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So, in the top box on this slide, we show that high-cost deposit product, high cost price at 150.
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And then we take a look at what a similar advance would be.
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The gross price would be 170, Daily Cash Manager about 170.
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But, taking out the dividend 156. So the marginal cost of borrowing wholesale, relative to a deposit special is six basis points.
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Now, we can compare that six basis point difference in marginal cost relative to the deposit profitability erosion
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that could happen from cannibalizing our existing book.
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So, if the overall existing book had a profitability of 2.82%, you can see here on the bottom, then it's cheaper to just take the high-cost deposit special
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because if there's no cannibalization whatsoever, that's cheaper funding and it's not going to affect the overall book.
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However, if there is any kind of cannibalization, then the overall profitability of the deposit book gets eroded.
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And if that erosion starts to be as much as the pricing difference, then it's not worth it.
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And what I think would often be found is that pricing on a high-cost deposit special would have to be at a level that would make introducing that special really challenging compared to an FHLB Advance.
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This final slide estimates how much of the balances in any given product type would have to migrate to the deposit special product before it would just make sense to borrow from the Federal Home Loan Bank.
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And it also recognizes the fact that that pricing difference between the deposits special, the high-cost MDA, and a short-term advance from the FHLB Boston, might not always be the same depending on the rate environment.
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So, as rates go higher, it's the likely assumption that the Daily Cash Manager advance, for example, will reprice fully with Fed Funds, whereas the deposit special, we said it had a 95% beta assumption baked into it, so it won't fully reprice.
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Also, advances will have a dividend that will adjust over time.
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So, we have to kind of recognize the fact that the relationship between the cost of the deposits special and the cost of an advance is not static --
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it can change over time. And we also recognize that the dividends are not entirely for sure. They could go up or down.
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But we can start to estimate what the cost of each one of these would be based upon the beta and based on an estimate of what advance pricing will be in the future.
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And then we can figure out how much would have to change in each one of our existing deposits,
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that is, how much would have to move from that existing deposit type into the deposit special, before it would make sense to borrow wholesale. And this slide does the math for us, and figures out: If 2.4% of DDA accounts move, enter the deposit special,
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you would have just been better off borrowing from the FHLB. That’s not including other types.
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So just on its own 2.4% of the DDAs moving to a high-cost deposit would have been economically damaging relative to taking wholesale funding.
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2.7% for NOWs, 3.5% for savings, and 4.5% for money markets are the other numbers. And again, that's not cumulative, that's just standalone.
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So, if some combination of these all changed, if each one of these moved about 1 percentage point, that on its own would also cause, um, the economic profitability of the deposit book to be so eroded
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that taking an advance would have been more beneficial.
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Thanks for your time today, and if there were any questions about this analysis, please feel free to contact me.