Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach
加密资产对系统性与特质风险的异质暴露：分而治之的方法AI 解读 ↓
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加密资产对系统性与特质风险的异质暴露：分而治之的方法
- 研究问题
- 在高频宏观金融风险代理缺失、低频候选指标众多时，如何区分加密资产收益对特质、市场及整体经济风险的异质暴露？
- 方法与思路
- 作者先用资产层面工具变量回归估计高频暴露，再从残差提取首个主成分并以高维变量选择关联低频指标，用均值组估计比较类别。
- 材料陈述的贡献
- 摘要报告短期均值回归及对特质波动和非流动性的平均暴露，并称绿色、DeFi 与稳定币类别呈现不同风险敏感度。
- 阅读关注点
- 核对工具变量有效性、残差因子的经济含义与频率匹配方式，检查资产分类、均值组推断及类别差异的稳健性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未报告样本期间、工具变量或系数数值，不能据此解释为因果风险效应。
原文摘要
This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a two-stage ``divide-and-conquer'' approach. The first stage estimates exposures to high-frequency idiosyncratic and market risk only, using asset-level IV regressions. The second stage identifies latent economy-wide factors by extracting the leading principal component from the model residuals and mapping it to lower-frequency macro-financial uncertainty and sentiment-based indicators via high-dimensional variable selection. Structured patterns of heterogeneity in exposures are uncovered using Mean Group estimators across asset categories. The method is applied to a broad sample of crypto assets, covering more than 80% of total market capitalization. We document short-term mean reversion and significant average exposures to idiosyncratic volatility and illiquidity. Green and DeFi assets are, on average, more exposed to market-level and economy-wide risk than their non-Green and non-DeFi counterparts. By contrast, stablecoins are less exposed to idiosyncratic, market-level, and economy-wide risk factors relative to non-stablecoins. At a conceptual level, our study develops a coherent framework for isolating distinct layers of risk in crypto markets. Empirically, it sheds light on how return sensitivities vary across digital asset categories -- insights that are important for both portfolio design and regulatory oversight.