The Extremity Premium: Sentiment Regimes and Adverse Selection in Cryptocurrency Markets

TL;DR

This paper demonstrates that sentiment extremity, measured by the Crypto Fear & Greed Index, predicts higher spreads and uncertainty in cryptocurrency markets, revealing an 'extremity premium' linked to sentiment regimes beyond volatility effects.

Contribution

It introduces the concept of the extremity premium, showing sentiment extremity's predictive power on spreads and uncertainty, validated across multiple cryptocurrencies and market cycles.

Findings

Extreme sentiment regimes lead to higher spreads and uncertainty.

Sentiment extremity predicts excess uncertainty beyond realized volatility.

The extremity premium is sensitive to analysis methods and captures volatility-regime interactions.

Abstract

Using the Crypto Fear & Greed Index and Bitcoin daily data, we document that sentiment extremity predicts excess uncertainty beyond realized volatility. Extreme fear and extreme greed regimes exhibit significantly higher spreads than neutral periods -- a phenomenon we term the "extremity premium." Extended validation on the full Fear & Greed history (February 2018--January 2026, N = 2,896) confirms the finding: within-volatility-quintile comparisons show a significant premium (p < 0.001, Cohen's d = 0.21), Granger causality from uncertainty to spreads is strong (F = 211), and placebo tests reject the null (p < 0.0001). The effect replicates on Ethereum and across 6 of 7 market cycles. However, the premium is sensitive to functional form: comprehensive regression controls absorb regime effects, while nonparametric stratification preserves them. We interpret this as evidence that sentiment extremity captures volatility-regime interactions not fully represented by parametric controls -- consistent with, but not conclusively separable from, the F&G Index's embedded volatility component. An agent-based model reproduces the pattern qualitatively. The results suggest that intensity, not direction, drives uncertainty-linked liquidity withdrawal in cryptocurrency markets, though identification of "pure" sentiment effects from volatility remains an open challenge.