A new paper Released Thursday by a team of crypto researchers hoping to add to a body of work that will eventually identify ‘the Black-Scholes of Decentralized Financing (DeFi)’ – a comparison that allows investors and users to properly identify DeFi projects and potential profits value / loss statistics in popular DeFi industries such as liquidity mining.
Why is such a comparison important? On the face of it, liquidity mining is simple enough to explain: in exchange for providing liquidity to automated market makers such as Uniswap, users are rewarded with trading fees or governance tokens, often expressed in APY percentages.
However, users suffer “temporary losses” related to fluctuations in the demand for the trading pair, and a simple APY calculation on a user interface frontend is not enough to paint a complete picture of what the gains might look like to liquidity providers.
According to research by Tarun Chitra, founder and CEO of DeFi risk analysis company Gauntlet.Network and one of the three co-authors of When does the tail wag the dog? Bend and market making, liquidity mining is best viewed as a complex derivative.
⚠️ Paper Warning ⚠️
Question: Have you wondered what math is for the following?
a) Optimal amount of token to emit for agricultural incentives
b) Hedging of temporary loss with options
c) When are LPs not stretched?
– Tarun Chitra (@tarunchitra) December 17, 2020
“Most passive investment products often have non-trivial derivative-like exposure. For example, the collapse of ETF XIV in February 2018 (“volmageddon”) illustrated how some assets that are “passive” and “safe” have complex exposure, “Chitra explained to Cointelegraph.” Providing liquidity in AMMs is not so different, although it presents a new set of risks for holders. Liquidity providers always balance the fees earned (positive income) with large losses on price movements (negative, temporary loss). “
This complexity has led to the failure of many liquidity mining projects due to excessive incentives (“1e9% APY is not sustainable, too many LPs and no traders”), or under-incentives from developers not offering enough rewards to offset temporary losses. Ultimately, users and developers should see “agriculture as a complex derivative analog of creator-taker incentives on centralized exchanges.”
In addition, this new conceptual model can enable more advanced decision-making by liquidity providers, as well as more robust architectural frameworks for SMP developers.
“This document provides developers and designers with a principled way to deliver LP returns that make sense,” said Chitra. “APY only makes sense for fixed income assets (bonds), while derivative pricing makes MUCH more meaningful for something like liquidity provision. We hope this is the first in the series of many works trying to find the ‘Black-Scholes of DeFi’. “
According to Chitra, a DeFi equivalent of the Black-Scholes model has been successfully identified could also be the key to mass adoption of DeFi. Black-Scholes was developed in the 1980s to help investors find ways to value stock options correctly, and sparked a massive boom in derivatives trading.
Although it remains to be seen whether a new model can cut through the complexity of DeFi so neatly, this article seems like a promising first step.