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HomeFinancial Solutionssome of crypto’s challenges look strangely familiar – Bank Underground

some of crypto’s challenges look strangely familiar – Bank Underground

John Lewis

Cryptoassets and the crypto ecosystem as a whole has to face many of the same challenges as conventional assets and the regular financial system do. The same classic problems which are staple of economics textbooks (and history books), such as maturity mismatch, liquidity shortages, credibility, and collateral feedback loops. But whereas the conventional system has learned from the past and evolved to deal with them, much of the crypto ecosystem seems to have overlooked them. In this post I draw out the parallels between previous issues in the traditional financial system and recent crypto turbulence. I argue that when crypto goes wrong, it often goes wrong in strikingly conventional, even old-fashioned ways.

The current crypto ecosystem comprises many elements including unbacked cryptocurrencies, stablecoins (algorithmic and asset-backed) settlement mechanisms, payment platforms, exchanges and financial intermediaries. In what follows, I want to look how that ecosystem deals (or fails to deal) with some classic issues faced by any financial system.

Where’s the lender of last resort?

Let’s temporarily abstract from concerns about unbacked cryptoassets’ intrinsic worth, overvaluation and price volatility (which I discuss here), and just focus on maturity transformation. Any ‘maturity transformer’ faces maturity mismatch: their liabilities are liquid and redeemable on demand, but their assets are longer term and less liquid. That’s the same whether you’re a traditional bank taking in deposits and lending in fiat or crypto lender doing it in crypto (eg Celsius).

Normally, only a fraction of people want to liquidate. But one of the oldest problems in finance is when everyone turns up at once and wants to access their money. Then even a solvent lender will face a liquidity problem.

The first known bank run was on Stockholms Banco in 1660. Frost et al document that the start of central banks becoming emergency liquidity providers goes back to the mid 17th century. By 1866, Walter Bagehot formulated the now classic rationale for a lender of last resort (LoLR) after the demise of Overend Gurney, a broker that evolved into something like a money market fund. Observing the liquidity run and ensuing panic, Bagehot proposed his famous dictum: LoLR should lend to solvent but illiquid actors, freely, at a penalty rate on good securities, which stabilises both individual institutions and the system as a whole.

For fiat-based banks, central banks can be a LoLR by creating liquidity when no private actor is willing to do so. And this remains a key part of their toolkit. In addition, retail bank deposits are protected by deposit insurance.

But what central banks can do simply, quickly and flexibly for fiat money can’t happen for bank-like activities in unbacked cryptocurrencies whose supply is governed by a mining protocol like bitcoin or ethereum, or for stablecoins like Tether whose issuance requires some backing asset. And non-bank fiat-based maturity transformers which cannot directly access central banks are covered by liquidity requirements designed to ensure they can cope with larger redemptions.

Recent problems at Celsisus played out like a textbook bank run. Like Overend Gurney or the Stockholms Banco centuries earlier, they had to limit withdrawals because they couldn’t cover the outflows. Another strikingly familiar phenomenon was the contagion effects of one lender failing prompting pressure on others.

Collateral damage?

Shocks can get amplified in a conventional financial system via prices of assets used as collateral. In a classic paper, Bernanke, Gertler and Gilchrist showed how declining asset values render loans under-collateralised, which prompts a margin call. Borrowers must then either post more collateral – depressing asset prices further, often exacerbated by ‘fire sale‘ effects, or the loan gets called in, contracting credit conditions. Addressing this in the conventional financial system was part of the post 2008 crisis response. This looks very similar to the dynamic during the recent bitcoin bear market, exacerbated by automatic liquidation of positions under smart contracts, and by higher levels of leverage than are allowed in the traditional financial system and limited liquidity on exchanges. And in the crypto system, this is further exacerbated by the sheer volatility of many crypto prices – an undesirable side effect of the lack of a nominal anchor.

Collateral effects also operate in the other direction. Often, posted collateral has to be kept apart in ‘cold storage’ so that when the borrower repays, there is no risk its value has been eroded. (Similar to the way that UK renters deposits have to placed in a protected scheme by the landlord). But crypto lenders such as Celsius allowed collateral to be rehypothecated – ie the lender could then use the collateral itself and re-pledge that asset to another lender. The collateral then gets passed along with multiple claims on it. If any party in the chain gets into trouble, there can be a domino effect. Rehypothecation by shadow banks and others was identified as a problem after the 2008 crisis by Singh and Aitken and others. Financial intermediaries and policymakers then took steps to reduce it. But in the crypto sector, the practice has been freely used, with similar negative consequences.

Algorithmic currency pegs?

The literature on currency pegs is basically about the challenge of fixing the value of your own currency with respect to another. Holding it ‘down’ is done by printing more of your own currency, and selling it for the anchor currency to counteract upward pressure (and build up forex reserves to boot). Holding the price ‘up’ is harder and typically requires either having a large ‘war chest’ of the anchor currency, which can be used to purchase the domestic currency and/or raising interest rates to compensate holders of your own currency for devaluation risk, to stave off the risk of speculative attacks.

In the absence of either tool, algorithmic stablecoin issuers have typically used some promise of future payment to induce investors to stay. One approach is offering investors a share of seignorage revenue in some hoped-for future period, when upward pressure means the issuer has to print more of their own currency or earn seignorage revenue. As Ben Dyson points out, this is unlikely to persuade investors to stay in: it pays no coupon, offers no risk premium and if the currency never recovers coinholders risk a 100% loss of capital. A more variant of this was to offer some payment in the form of another cryptocurrency, eg Luna for Terra. But as Craig Pirrong and others have pointed out this has a ‘doom loop’ problem – printing more of the other currency reduces its value: in the case of Luna the algorithm expanded supply more than 20,000 fold, destroying its value. A similar dynamic played out between IRON and TITAN, where issuance of the latter exploded to meet redemption demand for the former.

Stablecoin currency boards?

Asset-backed stablecoins claim to fully back issuance 1:1 with the anchor currency, and offer redeemability at par (though sometimes with fees and frictions which can create problems). This is basically the crypto equivalent of a currency board. Some currency boards have been successful (eg Hong Kong or the Baltic States), usually because of sound macro fundamentals, having more than 100% backing to allow for market risk (if interest rates rise, bond prices fall below what you paid for them) and investing the money in ultra safe assets. But others such as Argentina collapsed due to a combination of default on the underlying asset (US dollar denominated Argentine government bonds), insufficient backing, and a large outflow from the domestic banking system (which the central bank can’t stop by LoLR operations because it can no longer issue its own currency freely). Recently, some asset-backed stablecoins such as Tether, Neutrino and USDD have broken their pegs, for varying amounts of time. And it’s difficult to gauge how robust pegs are because of opacity surrounding exactly what assets are backing the likes of Tether, Circle and others. And as Frances Coppola points out, Tether’s own T&Cs reserve the right to delay withdrawals or offer redemptions in assets other than dollars.


New assets don’t always mean new problems or new solutions. Ironically, despite being promoted as alternatives to traditional finance, the crypto ecosystem faces many of the same problems. Some challenges relate to the underlying currencies – ideally you want a currency with stable value whose quantity can be changed to supply liquidity. But unbacked cryptocurrencies like bitcoin or ethereum which are the cornerstones of the system have the opposite properties: unstable value and a quantity that can’t be easily changed.

Other challenges relate to the system as a whole. Typically those are asymmetric: in upswings no-one wants to get out, loans get repaid, there are no margin calls, liquidity is abundant and collateral prices are rising. It’s only in downswings these issues materialise, often at the same time. And crucially, the crypto ecosystem currently lacks many of the guard rails developed over time in the regular system (capital buffers, liquidity requirements, stress tests, lender of last resort, resolution frameworks etc) to deal with them. As such, I think it is much more vulnerable when those problems emerge.

John Lewis works in the Bank’s Research Hub.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

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