Paper: arXiv 2506.21100

Authors: Nektarios Aslanidis, Aurelio Bariviera, George Kapetanios, Vasilis Sarafidis

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces an innovative two-stage ‘divide-and-conquer’ econometrics framework combining IV regressions, PCA, and high-dimensional boosting to solve mixed-frequency factor latent models in crypto assets. Its empirical execution is exceptionally rigorous, covering over 80% of total crypto market capitalization and benchmarking against diverse macro-financial indicators across multiple subgroups.

Research Flowchart

  flowchart TD
    A[Research Goal: Quantify Latent Systematic & Idiosyncratic Risk Exposures in Crypto] --> B[Data Inputs: High-Frequency Crypto Returns 80%+ Mkt Cap & Macro Indicators]
    B --> C[Stage 1: High-Frequency IV Regressions for Asset Market & Idiosyncratic Risk]
    C --> D[Stage 2: PCA on Residuals + Lasso/Elastic Net to Identify Macro-Financial Factors]
    D --> E[Mean Group Estimators across Asset Categories: Green, DeFi, Stablecoins]
    E --> F[Key Findings: Green/DeFi more exposed to market/macro risk; Stablecoins less exposed; Idiosyncratic volatility dominates]