What are the risks in DeFi?
The appropriate risk-return trade-off depends on an investor's risk tolerance. It is important to understand all the risks involved with DeFi that are powering the higher potential rewards.
Since every protocol or application is built with smart contracts interacting with one another in some form, smart contract failure in one of the core components could cause a ripple effect throughout the DeFi ecosystem.
This means that smart contract failure on the lower layers could lead to loss of funds in the protocols or applications that are built at the higher layers.
Overview of core DeFi components and composability
Stablecoins can fail due to collateralization and volatility risk together with flawed mechanism designs to maintain their respective pegs.
However, the main risks are associated with specific collateral types backing these stablecoins. Overcollateralization can help to reduce volatility risk but if the prices of the underlying collateralized asset drops too quickly, liquidation might not be enough to cover the full amount that was borrowed.
That being said, the risks are greatly reduced with a reasonable collateralization ratio and sound collateral types that have a monetary premium and deep liquidity like Ether or Bitcoin.
Losing a desired peg could cause loss of funds for stablecoin holders and that is why at Unagii we have decided only to work with the best, time-tested and widely accepted stablecoins like USDC (which is 1:1 backed by US dollars in a bank account issued by Coinbase) and DAI (decentralized crypto-overcollateralized stablecoin)
As with all new markets and technologies, other complex risks could exist with new Financial Platforms like Compound. One of these complex risks is Market Risk and better financial models are needed to better analyze these risks.
We use a rigorous definition of market risks to construct simulation-based stress tests that evaluate the economic security of the Compound protocol as it scales to underwriting billions of dollars of borrowed assets. These stress tests are trained on historical data and put through a battery of scenarios that represent the expected and worst case economic outcomes for the protocol. Our stress tests are constructed analogously to how transaction-level backtesting is done in high-frequency and algorithmic trading. These techniques are used to estimate the market risk of a systematic trading strategy before it is deployed to the market. As there are over $1 trillion US dollars of assets managed by funds that use these techniques to provide daily actuarial analyses to risk managers, we believe that these are the best methodologies for evaluating market risk. By modifying these techniques to handle the idiosyncrasies of cryptocurrencies, we are able to provide similar statistical power in these actuarial analyses
Here is their conclusion: