Matthew Raphaelson, our Chair of Financial Applications, was my student at Stanford in 1991. He went on to become CFO of a multi-billion-dollar organization at which he pioneered probabilistic thinking. He points out that SVB no doubt had stochastic models of both their investments and depositors. Individually each of these models might have indicated smooth sailing. When the right hand doesn’t know what the left hand is doing, but they both happen to be clapping, there is no sound. Had both models been driven by same stochastic library of interest rates, perhaps management might have noticed that the same event that would trigger large losses in their investments would trigger large withdrawals by their depositors, which is what put them under.
by Sam L. Savage
Banking, Liquidity, and the Likelihood of Simultaneous Failures
by Matthew Raphaelson - Download Bank Risk Model
Banks – like people – can survive all sorts of health issues for months, even years. A liquidity crisis, however, can be fatal to banks – and people – within days. For banks, liquidity means access to money, and they take great measures to have ready pools of funds to cover any emergency. So how did Silicon Valley Bank (SVB) in effect die of thirst?
Banks collect deposits from individuals and businesses and lend those deposits to other individuals and businesses to make money on the interest. Money lent out is not available to depositors; it cannot be withdrawn at the ATM or used to pay bills. So, banks reserve a portion of deposits not lent – this is known as liquidity.
Banks understand liquidity well, based on years of experience with patterns of customer behavior. They know paydays, when social security checks arrive, when rents are due. Since they do not earn much interest on liquidity, banks have a profit motive to maintain no more liquidity than is required by regulation.
Figure 1. Hypothetical bank liquidity model: “magical thinking” results in a modest liquidity cushion (difference between red line and blue line)
This model of bank liquidity works so long as customers continue to behave predictably, that nothing causes too many of them to withdraw all their money all at once.
The underlying assumption is that bank customers are a varied lot, and respond differently to whatever is going on in the economy. What if this assumption does not hold, if the entire customer base is sensitive to a single economic indicator, such as interest rates? Such was the case with SVB and its tightly-knit VC and tech customer base.
During a decade of what Harvard professor Mihir Desai calls “magical thinking”:
…the assumption that favored conditions will continue forever without regard for history. It is the minimizing of constraints and tradeoffs in favor of techno-utopianism and the exclusive emphasis on positive outcomes and novelty. [1]
Venture capital (VC) and tech funding increased by a factor of 10. At the same time, and for the same reasons, SVB deposits also increased by a factor of 10. When the Fed increased interest rates in 2022 and shattered the illusion of magical thinking, the cycle reversed – by Q4 2021 VC funding had fallen 66% from a year earlier. [2] SVB deposits fell $25 billion, or between 10-15%.
Figure 2. Once the interrelationship between interest rates, customer liquidity and bank liquidity is captured, it is clear the “magical thinking” liquidity model vastly under-estimates the true liquidity requirement (difference between red line and black line)
By itself, an interest rate-driven reduction in liquidity of less than 15% should not be fatal for SVB. Unless there was another interest rate-driven problem somewhere else in the bank. Unfortunately…there was.
Like many banks, SVP used deposits to invest in long-term bonds. These bonds pay higher interest, so the banks make more money.
When interest rates rise, these bonds lose value. This has been widely and correctly reported in the media, and the “unrealized losses” [3] look frighteningly large…in SVB’s case, large enough to wipe out all its capital.
Less well reported is what happens when the losses remain unrealized, meaning the bank is not forced to sell the bonds before they mature. Suppose the rest of the bank’s balance sheet is more valuable as interest rates rise. This occurs when most loans are variable rate and most deposits are fixed rate – in which case the bank can increase profits, despite the bond portfolio. This was exactly how SVB constructed its balance sheet.
SVB management may have believed that the bank was more profitable as interest rates rose…as long as there wasn’t some liquidity problem that forced the bank to sell bonds.
Figure 3a. Hypothetical bank with high unrealized losses but no additional liquidity risk. The bank has enough capital to absorb actual losses.
But there was a liquidity problem…a big one. The same factor – rising interest rates – which caused an unrealized losses in the bond portfolio simultaneously triggered the very liquidity problem that crystalized unrealized losses into actual losses.
Figure 3b. With interrelationships between interest rates, customer liquidity, bank liquidity, and bond values captured, insolvency is imminent.
If this weren’t bad enough, a few influential tweeters raised the alarm about SVB and may have unleashed a herd instinct among the close-knit customer base. The initial $25 billion of liquidity drain was followed by a $40 billion bloodletting in a period of a couple of days, which was not survivable.
Based on our experience, it would not be surprising if SVB had sophisticated interest rate risk models and liquidity coverage models that were nonetheless incoherent. Such models typically envision what would happen in hundreds or thousands of future parallel universes. Incoherence would occur if both models failed to capture the impact of rising interest rates in a consistent and coordinated way – one model can’t be in a universe of rising rates while the other is in a universe of stable or falling rates.
Summary: The Plight of Silicon Valley Bank
1. SVB’s bond portfolio was exposed to losses from rising interest rates, and the bank’s view on liquidity may have been influenced by magical thinking. Neither issue is unique to SVB.
2. SVB catered to an undiversified customer base whose own liquidity was weakened by rising rates. This does appear to be unique to SVB.
3. SVB apparently did not understand the extent to which rising interest rates would simultaneously result in unrealized losses and a liquidity crunch which would cause those losses to materialize.
This is illustrated in figure 3b – the distribution of customer withdrawals has “herded” to the right, triggering asset sales and high losses, and likely insolvency.
4. As financial markets recognized SVB’s dilemma, SVB’s close-knit customer base whipped into a panic via social media, accelerating the bank’s demise.
References:
[1] Mihir Desai, “The Crypto Collapse and the End of the Magical Thinking That Infected Capitalism,” The New York Times, 16 January 16, 2023
[2] EY.com, “Q4 2022 Venture Capital Investment Trends
[3] Unrealized losses are the losses that would occur if the assets are sold. If the assets are not sold, there are no losses. For example, suppose you bought a house for $250,000 and in today’s market the value was only $200,000. If you sold the house today, you would lose $50,000. But if you don’t sell, you don’t lose.
Copyright © 2023 Sam L. Savage