Crypto Pricing with Hidden Factors

TL;DR

This paper addresses whether cryptocurrency returns are driven solely by crypto-native risks or by latent, unobserved factors and traditional equity risk premia. It applies the Giglio-Xiu latent-factor model to weekly returns of about 253 top-cap cryptos from 2023 to 2024, comparing its risk premia to the standard Fama-MacBeth approach and incorporating non-tradable variables such as Altseason, Fear/Greed, and Hacks. The main findings show a substantial crypto market risk premium under the latent-factor approach ($$R_C$$ weekly premium of $0.471 ext{%}$, or about $24.5 ext{%}$ annually) and a large negative SMB_C premium (around $$-70.2 ext{%}$$ annually), along with positive premia for a Software sector factor and other equity-related exposures; Altseason is priced at a modest level, while Fear/Greed and Hacks have weaker or no explanatory power, and TVL’s premium is not robust under latent-factor controls. The results imply that crypto markets are increasingly integrated with traditional financial risks and that controlling for unobserved factors is crucial for accurate pricing, with implications for risk management and understanding transmission channels between crypto and traditional markets.

Abstract

We estimate risk premia in the cross-section of cryptocurrency returns using the Giglio-Xiu (2021) three-pass approach, allowing for omitted latent factors alongside observed stock-market and crypto-market factors. Using weekly data on a broad universe of large cryptocurrencies, we find that crypto expected returns load on both crypto-specific factors and selected equity-industry factors associated with technology and profitability, consistent with increased integration between crypto and traditional markets. In addition, we study non-tradable state variables capturing investor sentiment (Fear and Greed), speculative rotation (Altcoin Season Index), and security shocks (hacked value scaled by market capitalization), which are new to the literature. Relative to conventional Fama-MacBeth estimates, the latent-factor approach yields materially different premia for key factors, highlighting the importance of controlling for unobserved risks in crypto asset pricing.