Crypto market betas: the limits of predictability and hedging

Abstract This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged, market-neutral portfolios. We forecast 1-year-ahead market betas using various estimating methods, including ordinary least squares (OLS) and Vasicek’s Bayesian shrinkage estimator, and assess their impact on portfolio variance reduction across cryptomarket indices. Our findings indicate that while standard OLS betas explain significantly less of the variation in future betas for cryptoassets compared to US stocks, slope winsorization and Bayesian shrinkage improve prediction accuracy. The results suggest that beta-hedged portfolios reduce variance for approximately 17% of the universe, with the Broad Digital Market Index demonstrating the best hedging efficiency. These findings underscore the significant challenges of developing effective hedging strategies in the cryptocurrency market, emphasizing the importance of idiosyncratic risk in crypto returns and the need for appropriate market index representation.

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Publication Details

Journal
Financial Innovation
Published
2025-05-14
DOI
https://doi.org/10.1186/s40854-025-00777-w
Citations
2
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
2.21

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article

Crypto market betas: the limits of predictability and hedging

Michael L. Mark, Thomas A. Weber, Jan Šíla, Ladislav Krištoufek
2 citations
Financial Innovation
Financial Markets and Investment Strategies
2.21
article

Crypto market betas: the limits of predictability and hedging

Michael L. Mark, Thomas A. Weber, Jan Šíla, Ladislav Krištoufek
article en
2 citations

Abstract

Abstract This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged, market-neutral portfolios. We forecast 1-year-ahead market betas using various estimating methods, including ordinary least squares (OLS) and Vasicek’s Bayesian shrinkage estimator, and assess their impact on portfolio variance reduction across cryptomarket indices. Our findings indicate that while standard OLS betas explain significantly less of the variation in future betas for cryptoassets compared to US stocks, slope winsorization and Bayesian shrinkage improve prediction accuracy. The results suggest that beta-hedged portfolios reduce variance for approximately 17% of the universe, with the Broad Digital Market Index demonstrating the best hedging efficiency. These findings underscore the significant challenges of developing effective hedging strategies in the cryptocurrency market, emphasizing the importance of idiosyncratic risk in crypto returns and the need for appropriate market index representation.

Financial InnovationVol. 11(1)
Charles University (CZ), Czech Academy of Sciences, Institute of Information Theory and Automation (CZ), École Polytechnique Fédérale de Lausanne (CH)
Univerzita Karlova v Praze, Grantová Agentura České Republiky
Openalex Percentile: Top 14%
Financial Markets and Investment Strategies
2.21
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