Same Returns, Different Risks: How Cryptocurrency Markets Process Infrastructure vs Regulatory Shocks

Paper: arXiv 2602.07046

Authors: Murad Farzulla

Abstract

We investigate whether cryptocurrency markets differentiate between infrastructure failures and regulatory enforcement at the return level, complementing a companion conditional variance analysis that finds 5.7 times larger volatility impacts from infrastructure events (p = 0.0008). Using event-level block bootstrap inference on 31 events across Bitcoin, Ethereum, Solana, and Cardano (2019-2025), we find no statistically significant difference in cumulative abnormal returns between infrastructure failures (-7.6%) and regulatory enforcement (-11.1%): the difference of +3.6 pp has p = 0.81 with 95% CI [-25.3%, +30.9%]. This null acquires substantive meaning alongside the companion’s highly significant variance result: the same events that produce indistinguishable return responses generate dramatically different volatility signatures. Markets differentiate shock types through the risk channel – the second moment – rather than expected returns. The block bootstrap methodology, which resamples entire events to preserve cross-sectional correlation, reveals that prior parametric approaches systematically understate uncertainty by inflating degrees of freedom. Results are robust across eight specifications including permutation tests, leave-one-out analysis, and the Ibragimov-Mueller few-cluster test.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous empirical evaluation of how cryptocurrency markets process different types of shocks, using non-parametric block bootstrapping to correctly handle cross-sectional dependency. The findings offer a valuable contribution by showing that market differentiation between infrastructure and regulatory shocks operates through the second moment (volatility) rather than the first moment (returns). Reproducibility is exceptionally high with an explicitly linked code and data repository.

Research Flowchart

  flowchart TD
    A[Research Goal: Do crypto markets differentiate<br/>Infrastructure vs. Regulatory Shocks?] --> B[Data Inputs: 31 Events across<br/>BTC, ETH, SOL, ADA 2019-2025]
    B --> C[Methodology: Calculate Cumulative<br/>Abnormal Returns CAR]
    C --> D[Computation: Event-Level Block Bootstrap<br/>& Robustness Checks]
    D --> E[CAR Outcome: No Significant Difference<br/>Infra -7.6% vs Reg -11.1%, p=0.81]
    D --> F[Companion Outcome: Infrastructure has<br/>5.7x Volatility Impact, p=0.0008]
    E & F --> G[Key Finding: Markets differentiate shocks through<br/>Risk Channel Second Moment, not Expected Returns]

Cited References (103)

1. Pseudoreplication and the design of ecological field experiments (1984)
2. A critical investigation of cryptocurrency data and analysis (2020)
3. Uncertain Regulations, Definite Impacts: The Impact of the U.S. Securities and Exchange Commission’s Regulatory Interventions on Crypto Assets (2025)
4. Regulating cryptocurrencies: Assessing market reactions (2018)
5. Measuring economic policy uncertainty (2016)
6. The inefficiency of Bitcoin revisited: A dynamic approach (2017)
7. Asymmetric volatility in cryptocurrencies (2018)
8. Controlling the false discovery rate: A practical and powerful approach to multiple testing (1995)

0 of 103 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.