Crypto Pricing with Hidden Factors

Battle Count: 70

The paper provides actionable insights for crypto quantitative strategies: (1) the crypto size premium (~22.4% annualized) suggests systematic long-small-cap/short-large-cap crypto strategies; (2) the integration of crypto with equity factors (Software, RMW, stock market) implies cross-asset hedging and factor-neutralization strategies; (3) the Fear/Greed sentiment factor shows negative pricing, suggesting contrarian sentiment strategies; (4) the latent factor approach reveals that conventional Fama-MacBeth estimates materially misprice key factors, which is critical for risk model calibration. However, the short sample and exploratory nature limit direct strategy deployment.

The Giglio-Xiu three-pass estimator with unbalanced panel handling (iterative PCA imputation), Bai-Ng IC factor selection, moving-block bootstrap with 1000 replications, elastic net regularization with cross-validation, and stability selection framework requires substantial statistical programming. The factor construction (TVL orthogonalization, token-migration corrections, AR(1) residualization of non-tradable factors) adds further complexity. However, the individual components are well-documented in the literature and standard statistical packages support most building blocks.

Data sources are publicly available (CoinMarketCap API, Kenneth French data library, DeFiLlama, CVX website). Implementation details for the unbalanced panel, latent-factor estimation, and bootstrap procedure are in the Supplementary Appendix. However, no code repository is provided, and the Giglio-Xiu three-pass implementation with unbalanced panel handling requires significant custom coding. The elastic net and stability selection procedures are standard but the specific cross-validation and bootstrap configurations need careful replication.