Collateral Portfolio Optimization in Crypto-Backed Stablecoins

TL;DR

The paper tackles peg stability in crypto-backed stablecoins under collateral price shocks. It develops a dynamic, data-driven portfolio optimization framework that minimizes downside risk using both mean-variance and semivariance criteria, leveraging the covariance matrix $C$ and the constraints $\,\sum_i a_i = 1$, $0 \le a_i \le \lambda_i$. It applies the method to Dai collateral data, showing that increasing exposure to (wrapped) Bitcoin and reducing Ether improves resilience compared with the historical mix dominated by ETH and RWAs. The authors release data and code publicly and discuss practical enforceability in hybrid versus pure crypto-backed designs, outlining directions for future work on window length and rebalancing costs.

Abstract

Stablecoins - crypto tokens whose value is pegged to a real-world asset such as the US Dollar - are an important component of the DeFi ecosystem as they mitigate the impact of token price volatility. In crypto-backed stablecoins, the peg is founded on the guarantee that in case of system shutdown, each stablecoin can be exchanged for a basket of other crypto tokens worth approximately its nominal value. However, price fluctuations that affect the collateral tokens may cause this guarantee to be invalidated. In this work, we investigate the impact of the collateral portfolio's composition on the resilience to this type of catastrophic event. For stablecoins whose developers maintain a significant portion of the collateral (e.g., MakerDAO's Dai), we propose two portfolio optimization methods, based on convex optimization and (semi)variance minimization, that account for the correlation between the various token prices. We compare the optimal portfolios to the historical evolution of Dai's collateral portfolio, and to aid reproducibility, we have made our data and code publicly available.

Figures (4)

• Figure 1: Efficient frontier representing optimal portfolios consisting of selected cryptocurrencies for different values of expected return.
• Figure 2: Architecture of collateral-related smart contracts in the DSS. Squares indicate contracts and lines indicate relationships between contracts.
• Figure 3: Evolution of the relative (top) and absolute (bottom) contributions of the various collateral types among the total DSS collateral (measured in USD).
• Figure 4: Evolution of the optimal portfolios based on a 30-day window (top), 60-day (middle), or 200-day (bottom) of information.