bibtype J - Journal Article
ARLID 0646138
utime 20260224161453.6
mtime 20260217235959.9
SCOPUS 105005093210
WOS 001488135200001
DOI 10.1186/s40854-025-00777-w
title (primary) (eng) Crypto market betas: the limits of predictability and hedging
specification
page_count 28 s.
media_type E
serial
ARLID cav_un_epca*0525779
ISSN Financial Innovation
title Financial Innovation
volume_id 11
publisher
name Springer
keyword Asset pricing
keyword Market beta
keyword Cryptocurrency
keyword Crypto
keyword Market risk
keyword CAPM
keyword Bitcoin
keyword Beta-hedging
author (primary)
ARLID cav_un_auth*0457462
name1 Šíla
name2 Jan
institution UTIA-B
full_dept (cz) Ekonometrie
full_dept (eng) Department of Econometrics
department (cz) E
department (eng) E
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0503884
name1 Mark
name2 M.
country CH
author
ARLID cav_un_auth*0256902
name1 Krištoufek
name2 Ladislav
institution UTIA-B
full_dept (cz) Ekonometrie
full_dept Department of Econometrics
department (cz) E
department E
full_dept Department of Econometrics
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0403292
name1 Weber
name2 A.
country CH
source
url https://library.utia.cas.cz/separaty/2026/E/kristoufek-0646138.pdf
source
url https://link.springer.com/article/10.1186/s40854-025-00777-w
cas_special
project
project_id 24/SSH/020
agency GA UK
country CZ
ARLID cav_un_auth*0467327
project
project_id SVV 260 843
agency Charles University
country CZ
ARLID cav_un_auth*0503886
project
project_id GA23-06606S
agency GA ČR
country CZ
ARLID cav_un_auth*0458718
abstract (eng) 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.
result_subspec WOS
RIV AH
FORD0 50000
FORD1 50200
FORD2 50206
reportyear 2026
num_of_auth 4
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0375905
cooperation
ARLID cav_un_auth*0343541
name Charles University in Prague, Faculty of Science
country CZ
cooperation
ARLID cav_un_auth*0487074
name Ecole Polytech Fed Lausanne, Inst Mat Sci & Engn, CH-1015 Lausanne,
country CH
confidential S
article_num 107
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arlyear 2025
mrcbU14 105005093210 SCOPUS
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mrcbU63 cav_un_epca*0525779 Financial Innovation Roč. 11 č. 1 2025 2199-4730 Springer