Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach

2025-06-26

AI Summary

This study identifies and quantifies heterogeneous exposures of major crypto-assets to systemic versus idiosyncratic risk. Addressing the dual challenge of scarce high-frequency macro proxies and low explanatory power of low-frequency indicators, we propose a two-stage “divide-and-conquer” methodology: first, constructing a multilayered theoretical risk framework that maps high-frequency residuals onto low-frequency macro factors via high-dimensional variable selection; second, employing instrumental-variable regression, principal component analysis, and mean-group estimation to characterize cross-category (e.g., green assets, DeFi tokens, stablecoins) differences in risk sensitivity. Empirically covering crypto-assets comprising over 80% of total market capitalization, we find significantly higher systemic exposure for DeFi and green tokens, and markedly lower exposure for stablecoins. We further document robust short-horizon mean-reversion dynamics and consistently positive sensitivities to idiosyncratic volatility and liquidity risk.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Reasoning under Uncertainty: Causality
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Cryptocurrency and smart contracts
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems

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.

Problem

• Estimates heterogeneous exposures to crypto asset risks
• Identifies latent economy-wide factors in crypto markets
• Analyzes risk sensitivity variations across digital assets

Innovation

• Two-stage divide-and-conquer risk estimation approach
• Principal component extraction for latent factors
• High-dimensional variable selection mapping