Coverage Markets 2.0
A first of its kind Structured Financial Product Primitive on Defi
Last updated
A first of its kind Structured Financial Product Primitive on Defi
Last updated
Coverage markets 2.0 are a structured financial product which opens up the avenue to hedge against the real time risk score of protocols without taking any direct exposure in them.
These markets are designed to facilitate a leveraged lending & borrowing experience where traders can lock in ETH LST collaterals for a set time and borrow RI$K tokens at a leveraged rate using which they can further buy a leveraged call options on predictions.
Chainrisk risk engine generates a loss exceedance curve ( quantified impact v/s breach likelihood ) by running thousands on Agent-Based Monte-Carlo Simulations. Using this, Chainrisk generates a " Risk Score " for the protocol on a scale of 0-10 that signifies the current economic risk of the protocol. This Risk Score keeps fluctuating at a sub-minute level. A higher Risk Score signifies a more risky protocol.
Traders are able to take a long or short leveraged position at any given moment to predict any future increase or decrease in the protocol's Risk Score.
This section introduces a novel leveraged call options market within the Chainrisk Insurance AVS platform. This market allows users to speculate on the decrease in risk scores of DeFi protocols using leverage. Users can stake LST tokens (the platform's token) and borrow Risk tokens to amplify their potential gains. We present a mathematical framework to analyze potential returns and risks associated with this market.
LST: Amount of LST staked by a user
Y: Annualized base yield on LST staking (as a decimal)
LTV: Loan-to-Value ratio for Risk borrowing
Risk: Amount of Risk tokens borrowed by a user (Risk = LST * LTV)
C: Price per leveraged call option contract (in Risk tokens)
M: Number of leveraged call options purchased by a user
λ: Leverage multiplier of the call options (e.g., 2x, 5x)
S: Current risk score of a DeFi protocol (on a defined scale)
K: Strike price of the leveraged call option contract (represents predicted risk score)
P: Payoff per Risk token for a leveraged call option contract (if S < K)
A user stakes LST tokens and borrows Risk tokens as follows:
Risk Borrowing: Risk = LST * LTV (1)
The user utilizes the borrowed Risk tokens to purchase leveraged call options:
Number of Options: M = Risk / C (2)
If the actual risk score (S) of the DeFi protocol decreases below the strike price (K) of the leveraged call option by the expiry date, the user receives a profit:
Profit per Option: P = λ (K - S) (3)
Total Profit: Total_Profit = M * P (4)
Example
User Alice stakes 15,000 LST (LST = 10,000) with an annualized yield of 30% (Y = 0.05).
The protocol allows an LTV ratio of 0.66 (Overcollateralised).
Alice borrows Risk tokens (Risk): Risk = LST * LTV = 15,000 * 0.66 = 10,000 Risk.
Alice decides to borrow entire funds (10,000 Risk)..
Leveraged call options cost 20 Risk tokens each (C = 20) and offer 5x leverage (λ = 5).
Alice purchases M = Risk / C = 10,000 Risk / 20 Risk/option = 500 leveraged call options.
Scenario 1: Correct Prediction (Risk Score Decreases)
The current risk score (S) is 75 and the strike price (K) is 70.
After one month, the risk score of the protocol actually decreases to 65 (S = 65).
Profit Calculation:
Payoff per option (P): P = λ (K - S) = 5 (70 - 65) = 25 Risk tokens.
Total profit (Total_Profit): Total_Profit = M * P = 500 options * 25 Risk/option = 12,500 Risk tokens.
Scenario 2: Wrong Prediction ( Risk Score Increases )
The call options contract becomes invalid
Chainrisk protocol buys back the 10,000 Risk by selling off an equivalent amount of LST collateral. Rest of the collateral goes back to the user.
Leverage can significantly amplify both profits and losses. Liquidation penalties might apply if the borrowed Risk value falls below a minimum threshold relative to the staked LST value (based on a Maintenance Collateral Ratio).
This section focuses on statistically analyzing the risk associated with the leveraged call options market to prevent bad debt:
Historical Risk Score Volatility: We utilize the standard deviation (σ) of historical risk score changes for the target DeFi protocol to estimate potential variations in the actual risk score (S) by the expiry date.
Value at Risk (VaR): Value at Risk (VaR) is a statistical measure of potential losses with a given probability level (e.g., 95%). We can calculate the VaR for the leveraged call options position based on the following:
Expected Payoff: Assuming a normal distribution of historical risk score changes, we can calculate the expected payoff (E) for the leveraged call options using the Black-Scholes model or similar option pricing models. This considers factors like the current risk score (S), strike price (K), leverage (λ), volatility (σ), and time to expiry.
Confidence Interval: We can then define a confidence level (e.g., 95%) to estimate the potential range of losses. The VaR at this confidence level represents the maximum potential loss that might occur with a 95% probability.
Risk Management Strategies:
Dynamic LTV Ratios: The platform shall implement dynamic LTV ratios based on the historical volatility (σ) of the target DeFi protocol's risk score. Lower LTV ratios are enforced for protocols with higher historical volatility, reducing the potential for bad debt due to excessive leverage.
Margin Calls and Liquidation Thresholds: The platform shall implement margin calls and liquidation mechanisms based on the VaR calculations. If the potential loss (based on VaR) exceeds a certain threshold relative to the staked LST value, a margin call might be triggered, forcing the user to add more LST or face liquidation of a portion of their staked LST to cover potential losses.
This paper presents a mathematical framework for the leveraged call options market within the Chainrisk Insurance AVS platform. Users can leverage their predictions on decreasing risk scores for potentially amplified returns. However, careful risk management is crucial due to the potential for significant losses associated with leverage and liquidation.
Explore incorporating risk score volatility into the option pricing model.
Analyze the impact of different leverage multipliers and LTV ratios on potential returns and risks.
Develop a dynamic risk management system to mitigate liquidation risks for users.
This framework provides a foundation for further research and development of the leveraged call options market within the Chainrisk Insurance AVS, fostering a more sophisticated risk management ecosystem for DeFi users.