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

TL;DR

This study examines how cryptocurrency markets respond differently to infrastructure failures versus regulatory shocks, finding similar average returns but significantly different volatility impacts, highlighting the importance of second-moment analysis.

Contribution

It demonstrates that markets do not differentiate between shock types in returns but do in volatility, using a novel bootstrap approach to accurately assess uncertainty and risk channels.

Findings

No significant difference in cumulative abnormal returns between shock types

Infrastructure events cause larger volatility impacts than regulatory shocks

Bootstrap methodology reveals higher uncertainty than parametric methods

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.