0599014 20260224154314.120241007235959.9 85196257573 001250706200001 10.1080/13504851.2024.2363295 8 s. P cav_un_epca*02501181350-48513221 (2025)3113-3120Routledge Cryptoasset GARCH Garman-Klass volatility cryptocurrency cav_un_auth*0474007 Sun W. CZ cav_un_auth*0256902 Krištoufek Ladislav UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/E/kristoufek-0599014.pdf https://www.tandfonline.com/doi/full/10.1080/13504851.2024.2363295 GA23-06606S GA ČR CZ cav_un_auth*0458718 Cryptoassets are extremely volatile with possible volatility jumps and infrastructure noise, making the estimation of true volatility process challenging. When the high-frequency data are not available, the true volatility needs to be estimated to be further studied or forecasted. The GARCH-family models have become a norm in the field. Here, we examine the performance of 6 GARCH-type specifications with 4 distributional assumptions and compare them with 4 non-parametric range-based models built on the daily ‘candles’. Our study focuses on five popular cryptocurrencies (Bitcoin, Ethereum, BNB, XRP, and Dogecoin) between 1 July 2019 and 30 September 2022, utilizing Binance 5-minute data for realized measures as the high-frequency estimators of the true volatility process. The results reveal that the Garman-Klass estimator clearly outperforms the GARCH-family models in all studied settings, and the other range-based estimators remain competitive with the GARCH-family models. These results are crucial for studies on volatility in cryptoassets where using the GARCH-type models is a standard. When the high-frequency data are not available, the range-based estimators, and the Garman-Klass estimator in particular, should be preferred as proxies for the true volatility process over the GARCH-type models, be it in the in-sample, more qualitative studies, or the forecasting, out-of-sample exercises. WOS AH 50000 50200 50206 2026 2 RVO:67985556 https://hdl.handle.net/11104/0357192 S C ECONOMICS 1.3 0.4 5.2 0.00507 0.28 6139 67 0.384 14.09 1.64 2170 Q3 1.100 587 Q3 47 0.37 Q3 46.5 2025 85196257573 SCOPUS PUBMED 001250706200001 WOS cav_un_epca*0250118 Applied Economics Letters 32 21 2025 3113 3120 1350-4851 1466-4291 Routledge