Variance Decomposition and Cryptocurrency Return Prediction

Suzanne S. Lee and Minho Wang

Journal of Financial and Quantitative Analysis, 2025, vol. 60, issue 4, 1859-1890

Abstract: This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

Date: 2025
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