加密货币市场是否将基础设施冲击与监管冲击区分开来？基于多矩事件研究的依赖性稳健推断
Do Cryptocurrency Markets Differentiate Infrastructure from Regulatory Shocks? A Multi-Moment Event Study with Dependence-Robust Inference
AI总结：
本文研究加密货币市场对基础设施故障和监管冲击的反应差异，采用GJR-GARCH-X模型进行多矩事件分析，揭示了依赖性稳健推断下的显著性差异及选择偏差问题。
摘要：
加密货币市场是否将基础设施故障与监管冲击区分开来？我们研究了回报分布的两个矩值，在一个共享样本（50个事件，6种资产，2019-2025年）中拟合GJR-GARCH-X模型，采用匹配依赖性稳健推断。我们将事件包含视为测量设计参数：而不是断言排除依赖变量选择偏见，我们追踪了包含筛选中的方差差异并直接测量选择偏差。结果是一个范围条件——在经过筛选、高相关性识别下，差异显著（4.88倍），但选择条件性：对广泛重建池进行机械影响过滤后，将其缩小到1.3-1.6倍。识别是故事的一半；推断是另一半。经过筛选的乘数在考虑跨资产依赖性和厚尾时无法区分于零：学生t-协同体CCC-GARCH-X自助法（我们的推断记录）返回p≈0.32，由于六个资产的系数强烈跨相关，对比的有效样本量接近三而非六（设计效应p≈0.07-0.15）。朴素的i.i.d.测试曾报告出看似决定性的五倍效应，但该显著性是伪重复的产物：在相关资产间伪重复叠加厚尾误设的自助法。第一矩讲述相同的故事——一个+7.19 pp累计异常回报差异，块自助法无法区分于零（p=0.283）。在正确推断下，不对称性是方向性的但未解决。贡献是一个便携推断工具包——一个推断梯度和蒙特卡洛大小研究——用于诊断厚尾市场中的跨资产事件研究如何制造显著性，展示了其在五倍结果中溶解的地点，该结果作者本人曾发表过。
英文摘要：
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.
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