Dan Larimer recently introduced ideas for a new stablecoin pegging algorithm based on the Bancor protocol. The newly launched Equilibrium Labs, the research and development arm of the Equilibrium framework, is researching price-stable protocols and tokens for the decentralized finance market. They provide some insights into the main issues that threaten the stablecoin market’s adoption of the technology and products based on it.
Understanding the Bancor protocol
The Bancor protocol is an open-source standard that lets every token connected to the protocol be automatically convertible to any other connected token, turning them into Smart Tokens in the process. The Bancor Network Token (BNT) is the hub token through which liquidity pools in the Bancor Network interconnect. These liquidity pools, known as Bancor relays, form Bancor’s decentralized “liquidity network”, where integrated tokens are instantly convertible for one another, without the end user ever needing to hold BNT.
Each Bancor relay manages two connectors, each containing a quantity of tokens. The prices a relay offers users reflect the ratio of token quantities held in each connector. For example, a Bancor Relay between the Equilibrium framework’s stablecoin (EOSDT) and BNT will show a price for BNT/EOSDT based solely on the supply and demand of both tokens in the Bancor Network.
What is Larimer’s idea about?
Larimer designed a Bancor Relay analog that is actually a wrapper around this interlink token. It’s a token that can convert one token’s format to another, but with added profit-sharing and security token features — he called it MMS. It has two connectors as a Bancor Relay wrapper — one for the base asset (the underlying assets that yields value) and one for the quote asset (the paired asset it trades against). The market value of each connector is based on a trusted price feed (or a 24-hour median), and the system’s objective is to keep the market value of both connectors equal, or close within a tight range of ± 2%. This is done by managing excess collateral reserves and the quote asset supply. By construction, the price produced is close to the trusted feed price.
Pros and contras of the Larimer’s solution
Despite the name for these “market-making tokens,” the Bancor protocol and the pegged algorithm aren’t yet market-making frameworks. Market-making mechanisms primarily seek to make profit, and they should hold a concept of risk. Without risk, there’s no incentive for the framework to generate revenue. Reciprocally, there is no revenue without risk: there is no free-lunch.
Larimer points out that those holding MMS tokens are “willing to be slightly leverage-long in a collateral asset (such as EOS).” In other words, they’re the ones holding inventory risk. So what are their preferences and incentives?
These people would seek to maximize profits under inventory risk constraints — market-makers should be rewarded for tolerating their inventory risk. But they also have other preferences: they would be concerned about running out of inventory, for example, in which case they wouldn’t be able to quote both a bid (if they ran out of the quote asset) and an ask (if they ran out of the base asset).
The Bancor protocol is a decentralized exchange mechanism that lets anyone show a price that’s transparently determined and transparently backed. It’s the conceptual backbone of an exchange. The price ratio has to reflect a ratio of tangible supplies for a market participant, but the ratio of tangible supplies could potentially reflect that participant’s preferences.
In other words, the price that Bancor produces is only the tip of the iceberg — it could simply be the price that a market maker decides to publish, or the peg that a Central Bank would like to hold. We know the Bancor price is not a bogus price because it’s backed by actual quantities, but the quantities can be adjusted by the market-maker or the central bank to reflect their preferences or their decisions.
Market prices: in love with variety
A Bancor price is completely determined by the number of tokens available in its connectors. The price dependency to the ratio of token quantities is reminiscent of the Dixit-Stiglitz model, which lives at the heart of some of the most advanced economic models. It follows a simple hypothesis: economic agents love variety.
In economic theory, the agent’s preferences are modeled by a function of quantities called a utility function. A thoughtful agent will maximize his utility under at least one constraint: that of budget. The solution to the utility function gives the indifference price between both assets.
The Bancor algorithm is a limit case of the Dixit-Stiglitz model. Because inventories can never deplete, there will always be a mix. For similar connector positions, Bancor will always show the same consensus price, regardless of preferences. If the connector quantity of an asset drops, the Bancor price for the asset will soar. The price movement could revert if an external seller shows up, because he believes the movement is unsupported by selected fundamental or behavioral indicators.
Dixit-Stiglitz lets the experienced market-makers show a more aggressive offer hoping to sell ahead of the external seller, should they believe that the price will revert to its mean. It gives market-makers more freedom.
Dan Larimer’s MMS concept fundamentally shows us how to design market-making smart contracts around a reliable and transparent Bancor framework. Like the simple Dixit-Stiglitz proposition, we can explore the structure’s potential and build serious incentives for anyone to buy MMS tokens (or conceive their own), trusting that their utility will be maximized.
Dan Larimer’s structure, as a whole, can be enhanced for the design of advanced market-making strategies. However, market-making is not the only potential that Dan Larimer’s model holds. Some of the structure’s features, considered separately, also have exciting applications for any asset-backed stablecoin framework. One of the most simple, yet radical, features introduced could help an asset-backed stablecoin control potential hyperinflation.
In case of depreciation, a central bank with a pegged currency policy can use its foreign currency reserves to buy back and support its local currency. Larimer’s pegging algorithm would similarly buy back stablecoins with the excess collateral. It is challenging: Would a stablecoin position holder accept to see his excess collateral sold in an inflationary market, when most likely, the collateral holds its best potential for performance?
A softer option could be to enforce stability fee payments to be paid in the collateral currency, at regular intervals. These payments would be used to buy back and burn stablecoins. Is it effective enough? These are some of the options we will explore with our newly launched Equilibrium labs.