Crypto borrowing and staking networks

Jake Brukhman
The CoinFund Blog

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Introduction

As decentralized staking networks continue to develop and proliferate, it is interesting to consider their impact on other areas of the crypto economy. One area where the vocation of staking is bound to have an impact is cryptoasset borrowing.

Today, the foremost examples of staking networks in production include Tezos, Livepeer, and even SpankChain. These networks rely on staking to provide security and governance. They also provide an antidote to high velocity, which, if such a phenomenon ever manifests in actual cryptoeconomics, is curbed by taking assets out of circulation and solves the main foil of localized digital currencies and payment tokens.

Staking networks today are being bootstrapped by early investors and funds who typically hold sizable network ownership based on investment in early project stages. But most funds find it difficult to technologically engage networks, especially large numbers of them, and look to service providers for security, custody, and domain-specific utilization of assets across systems. However, staking service providers themselves don’t hold early ownership of networks in general. They find themselves in a position where they must rely on delegation of assets (a limiting factor, since delegation is not available on-protocol in all networks) or fund and investor relationships for access.

However, as lending platforms and protocols come online, staking companies have new options for commanding larger stakes. Borrowing assets for the purpose of staking will serve two key purposes in the staking space: (1) access to the network, as most networks will require using tokens to perform work or run functional nodes; and (2) ability to create asset-neutral staking positions.

Asset-neutral portfolios

Staking networks are great — they create a new source of returns backed by real economies of decentralized network participants. But while most opportunities fall into a 5–20% return range in a token-denominated sense, the fiat-denominated returns are nevertheless much more volatile. The fiat-denominated return of a staking opportunity still depends more on sentiment and network growth than the typical rate of token return.

Borrowing tokens on decentralized protocols may in the future provide a simple way to lower fiat-denominated downside while staking operations are taking place. Suppose you wanted to earn a return on a staking network which offers a token-denominated 5% rate of return without being exposed to its fiat-denominated volatility. If you can borrow the network asset at a 1% token-denominated rate, you can stake it in the network, earn 5%, and then keep 4% for yourself. Borrowing the asset has a similar effect to shorting, neutralizing the long (fiat market) exposure to the asset.

Further opportunities are available to curb the fiat-denominated volatility of the position as well. For instance, the token-denominated return can be periodically skimmed and liquidated to lower volatility exposure.

Since being able to borrow tokens is useful in the context of staking, we might envision a market where staking opportunities are creating a lot more borrowing demand than we see now. So what is the potential relationship between borrowing rates and returns of networks?

Borrowing rates and staking returns converge

Today, borrowing rates on Compound Finance are rather low as compared to prospective staking opportunities. For instance, Livepeer’s current daily inflation rate (paid to transcoders and delegators as a bonding incentive) annualizes to over 25.8%. But the borrow APR of BAT, the most expensive asset borrowable on Compound, is 8.25%.

As demand increases for staking returns, this kind of differential is bound to bring borrowing rates higher. If a significant portion of staking begins to rely on borrowed tokens (due to stakers seeking access and asset-neutral portfolios built by funds), then lenders will push up rates accordingly.

Much of on-chain borrowing happens with collateralized smart contracts today, and underwritten or fractional-reserve systems that don’t require collateral have been proposed. Decentralized lending may carry default risk, in addition to technological and hacking risks inherent to smart contracts. Similarly, staking activities carry the risk slashing conditions (situations where staking nodes can lose tokens due to downtime or behavior that doesn’t comply with the protocol).

While the spread between borrowing rates and staking returns converges, it should still be non-zero to compensate these types of risks.

High borrowing rates

What if borrowing rates were higher than staking returns? This seems to create an incentive to withdraw stake from staking networks in order to earn a higher return through lending, creating a potential disincentive for staking activities. Whether this can or might happen is a case by case analysis of individual networks — for example, in Livepeer, the inflation rate would adjust as a function of staking participation rate (p-rate) to encourage more delegation and bonding.

Oversized demand for borrowing may also be an indicator of growing short interest in an asset, so using comparisons of borrowing rates and staking returns may be an indicator of market sentiment with respect to an asset or network.

Conclusion

In summary, crypto borrowing may be an important tool for stakers, crypto-focused investors, and analysts assessing the health of staking network markets. Borrowing creates new avenues of access to networks for new entrants into the generalized mining space, as well as a mechanism to build asset-neutral portfolios of staking opportunities. The spread between borrowing rates and staking returns should generally converge together, leaving some spread to compensate for technological and protocol-specific risks like slashing. Both the staking space and the decentralized lending space are important elements of the crypto economy worth following.

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Founder @ CoinFund. Blockchain research & cryptoasset investments. #cryptoeconomics #generalizedmining Previously CTO @ Triton Research, TPM @ Amazon.