Market Inefficiency in Cryptoasset Markets

Battle Count: 84

The paper is highly relevant to quantitative trading in crypto/DeFi markets. It identifies persistent yield misalignments between ETH lending, staking, and liquid staking strategies that could inform relative value trading. The finding that stETH lending yield barely responds to ETH lending yield changes (β=0.017 vs predicted 1) suggests exploitable spread opportunities. However, the paper does not directly propose trading strategies or account for transaction costs, gas fees, and execution risks that would affect practical implementation. The equilibrium framework could be used to build signal-based strategies monitoring yield differentials.

The empirical implementation is relatively straightforward: daily yield data collection from public sources, computing yield differentials, and running two linear regressions with HAC standard errors. The theoretical framework (pricing kernel, Euler equations, Markov chain for peg risk) requires graduate-level asset pricing knowledge to understand but does not need to be implemented computationally for the empirical tests. The main complexity lies in data collection and ensuring correct yield calculations from on-chain sources.

The paper provides specific data sources (Aavescan, Dune Analytics, CoinGecko) with URLs, clear sample period (Jan 30, 2023 – Sep 21, 2025), 966 daily observations, and well-defined regression specifications with HAC standard errors (Newey-West, 10 lags). The theoretical framework is fully specified with proofs in the appendix. However, no GitHub repository is mentioned for code reproduction.