Crypto Pricing with Hidden Factors

TL;DR

This paper estimates risk premia in cryptocurrency returns by incorporating latent factors and novel sentiment and shock variables, revealing increased market integration and the importance of unobserved risks.

Contribution

It introduces a latent-factor approach to crypto asset pricing and incorporates new sentiment and shock variables, advancing understanding of crypto risk premia.

Findings

Crypto returns load on crypto-specific and equity-industry factors.

Investor sentiment and shocks significantly influence crypto risk premia.

Latent-factor approach alters key factor premia estimates.

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.