Do cryptocurrency markets respond differently to infrastructure and regulatory shocks? We study returns and conditional variance on a shared sample of 50 events and six assets (January 2019--August 2025), using GJR-GARCH-X models and dependence-aware inference. Treating event inclusion as a design parameter, we trace the variance differential across inclusion screens. Curated high-salience events yield a $3.49\times$ point-estimate multiplier, whereas a mechanical impact filter on a broad reconstructed candidate pool yields approximately $0.5$--$1.6\times$. This pattern is descriptive and selection-conditional, not an inferential comparison between screens. The curated variance differential is not significant against its fitted sharp per-asset-equality null under the conditional fixed-path Student-$t$-copula bootstrap ($p\approx0.39$). Floored recursive sensitivity gives one-sided $p=0.025$ at baseline and $0.041$ with asset-specific high-variance-regime controls, so the verdict depends on inference scheme and implementation. Across reported dependence inputs, the six-contrast effective sample size is $1.33$--$2.35$; one-sided effective-df sensitivity gives $p=0.044$--$0.116$. Earlier significance from treating correlated per-asset coefficients as independent samples is not robust to dependence and heavy-tail corrections. The cumulative-abnormal-return difference is $+8.69$ percentage points (event-level block-bootstrap $p=0.202$). The asymmetry remains directional, selection-conditional and unresolved. The contribution is an inference ladder and an internal Monte-Carlo calibration study, demonstrated through correction of the author's earlier significance claim.

翻译：暂无翻译