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 estim...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.展开更多
基金financial support from the Czech Science Foundation under the project`Deep dive into decentralized finance:Market microstructure,and behavioral and psychological patterns’[Grant No.23-06606S]supported by Charles University Research Centre program No.24/SSH/020+1 种基金the Cooperatio Program at Charles University,research area Economicsfinancial support from the Charles University Specific University Research scheme[Grant No.SVV 260843].
文摘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.
