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

Paper: arXiv 2602.07018

Authors: Murad Farzulla

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.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a rigorous empirical investigation into how sentiment extremity affects bid-ask spreads and uncertainty in crypto markets. It utilizes multi-asset data, Granger causality, robust statistical validation, and an agent-based model (ABM) calibrated via SMM, though the mathematical formulations rely primarily on standard statistical frameworks rather than novel theoretical derivations.

Research Flowchart

  flowchart TD
    A["Research Goal: How does market sentiment extremity<br>impact liquidity and adverse selection in crypto?"] --> B["Data Inputs: BTC/ETH daily data &<br>Crypto Fear & Greed Index (2018–2026, N=2,896)"]
    
    B --> C["Methodology & Computational Analysis:<br>• Nonparametric Volatility Stratification<br>• Granger Causality & Placebo Tests<br>• Agent-Based Modeling (ABM)"]
    
    C --> D{"Functional Form Sensitivity"}
    
    D -- "Stratification / ABM" --> E["Extremity Premium Validated:<br>Extreme Fear & Greed expand spreads<br>(p < 0.001, Cohen's d = 0.21)"]
    D -- "Parametric Regressions" --> F["Regime Effects Absorbed:<br>Hard to separate 'pure' sentiment<br>from embedded volatility"]
    
    E --> G["Key Outcome: Intensity (not direction) drives<br>uncertainty & liquidity withdrawal across cycles"]
    F --> G