A new paper released on Thursday from a team of crypto researchers hopes to add to a body of work that will eventually identify “the Black-Scholes of decentralized finance (DeFi)” — an equation that will allow investors and users to properly value DeFi projects and potential profit/loss metrics in popular DeFi verticals such as liquidity mining.
Why is such an equation important? At first blush, liquidity mining is simple enough to explain: in exchange for providing liquidity to automated market makers like Uniswap, users are rewarded with trading fees or governance tokens, often denominated in APY percentages.
However, users suffer "impermanent losses" related to fluctuations in demand for the trading pair, and a simple APY calculation on a user interface frontend isn't sufficient to paint a full picture for what the gains might look like for liquidity providers.
According to research from Tarun Chitra, founder and CEO of DeFi risk analysis firm Gauntlet.Network and one of the three co-authors of When does the tail wag the dog? Curvature and market making, liquidity mining is best thought of as a complex derivative.
“Most passive investment products often times have non-trivial derivatives-like exposure. For instance, the collapse of the ETF XIV in February 2018 (“volmageddon“) illustrated how some assets that are “passive” and “safe” have complex exposure,” Chitra explained to Cointelegraph. “Liquidity providing in AMMs is not so different, although it presents a new set of risks to holders. Liquidity providers are always balancing fees earned (positive income) with large price moves losses (negative, impermanent loss).”
These complexities have led to the failure of many liquidity mining projects due to overincentivization (“1e9% APY isn’t sustainable, too many LPs and no traders”), or underincentivization from developers not offering enough rewards to counterbalance impermanent losses. Ultimately, users and developers “should think of farming as a complex derivatives analogue of maker-taker incentives on centralized exchanges.”
Additionally, this new conceptual model may allow for more sophisticated decision making from liquidity providers, as well as more robust architectural frameworks for AMM developers.
“This paper provides a principled way for developers and designers to provide LP returns that make sense,” said Chitra. “APY only makes sense for fixed income assets (bonds), whereas derivative pricing makes MUCH more sense for something like liquidity provision. We hope this is the first in the line of many works that try to find the ‘Black-Scholes of DeFi.’”
According to Chitra, successfully identifying a DeFi-equivalent to the Black-Scholes model might also be the key to mass DeFi adoption. Developed in the 1980s to help investors find ways to properly price stock options, Black-Scholes led to a massive boom in derivatives trading.
While it remains to be seen if a new model can cut so cleanly through DeFi's complexities, this paper appears to be a promising first step.