| bibtype |
J -
Journal Article
|
| ARLID |
0599014 |
| utime |
20241023130525.6 |
| mtime |
20241007235959.9 |
| SCOPUS |
85196257573 |
| WOS |
001250706200001 |
| DOI |
10.1080/13504851.2024.2363295 |
| title
(primary) (eng) |
Beyond GARCH in cryptocurrency volatility modelling: superiority of range-based estimators |
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0250118 |
| ISSN |
1350-4851 |
| title
|
Applied Economics Letters |
| publisher |
|
|
| keyword |
Cryptoasset |
| keyword |
GARCH |
| keyword |
Garman-Klass |
| keyword |
volatility |
| keyword |
cryptocurrency |
| author
(primary) |
| ARLID |
cav_un_auth*0474007 |
| name1 |
Sun |
| name2 |
W. |
| country |
CZ |
|
| 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. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA23-06606S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0458718 |
|
| abstract
(eng) |
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. |
| RIV |
AH |
| FORD0 |
50000 |
| FORD1 |
50200 |
| FORD2 |
50206 |
| reportyear |
2025 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0357192 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
ECONOMICS |
| mrcbT16-j |
0.285 |
| mrcbT16-D |
Q4 |
| arlyear |
2024 |
| mrcbU14 |
85196257573 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001250706200001 WOS |
| mrcbU63 |
cav_un_epca*0250118 Applied Economics Letters available online 2024 1350-4851 1466-4291 Routledge |
|