| bibtype |
J -
Journal Article
|
| ARLID |
0646023 |
| utime |
20260213133527.1 |
| mtime |
20260213235959.9 |
| SCOPUS |
105027415372 |
| WOS |
001661114100003 |
| DOI |
10.1186/s40854-025-00808-6 |
| title
(primary) (eng) |
Event‑driven changes in return connectedness among cryptocurrencies |
| specification |
| page_count |
37 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0525779 |
| ISSN |
Financial Innovation |
| title
|
Financial Innovation |
| publisher |
|
|
| keyword |
Return connectedness |
| keyword |
Cryptocurrencies |
| keyword |
Bootstrap-after-bootstrap procedure |
| keyword |
Portfolio composition and hedging |
| author
(primary) |
| ARLID |
cav_un_auth*0503621 |
| name1 |
Albrecht |
| name2 |
P. |
| country |
CZ |
| garant |
K |
|
| author
|
| ARLID |
cav_un_auth*0312139 |
| name1 |
Kočenda |
| name2 |
Evžen |
| 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 |
|
| cas_special |
| project |
| project_id |
GA23-06606S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0458718 |
|
| abstract
(eng) |
Our study presents an in-depth analysis of the connectedness in returns among five major cryptocurrencies over a span from late 2017 to 2023. Our work introduces novel insights by employing a recently developed bootstrap-after-bootstrap method of Greenwood-Nimmo et al. (Econ Model, 140, 106843, 2024) to establish a link between increases in connectedness and various systematic events. We find that major events-including both market and policy-driven shocks-trigger substantial increases in connectedness, with transmission effects persisting for up to one month. For the period under research, we identify Bitcoin and Ethereum as net return transmitters, mainly to Binance coin and Ripple. Moreover, we find that these transmissions increased by up to 20% for up to one month after the shocks occurred. Furthermore, we incorporate event-driven adjustments in portfolio optimization, quantifying optimal asset weight rebalancing in response to cryptocurrency market shocks. Our findings reveal that during the research period, Cardano and Ripple were the most effective choices in portfolio optimization. The implications of this study are significant for devising strategies in portfolio management and risk hedging, offering valuable guidance for policy formulation in the financial sector. |
| reportyear |
2027 |
| RIV |
AH |
| result_subspec |
WOS |
| FORD0 |
50000 |
| FORD1 |
50200 |
| FORD2 |
50206 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0375790 |
| cooperation |
| ARLID |
cav_un_auth*0503328 |
| name |
Mendel University in Brno, Faculty of Business and Economics |
| institution |
Mendel Univ. |
| country |
CZ |
|
| cooperation |
| ARLID |
cav_un_auth*0503331 |
| name |
IOS, Regensburg |
| institution |
IOS |
| country |
DE |
|
| cooperation |
| ARLID |
cav_un_auth*0503334 |
| name |
CESifo, Munich |
| institution |
CESifo |
| country |
DE |
|
| cooperation |
| ARLID |
cav_un_auth*0503333 |
| name |
Institute of Economic Studies, Charles University, Prague |
| institution |
IES, CUNI |
| country |
CZ |
|
| confidential |
S |
| article_num |
20 |
| mrcbC91 |
A |
| mrcbT16-e |
BUSINESS.FINANCE|SOCIALSCIENCES.MATHEMATICALMETHODS |
| mrcbT16-f |
7.5 |
| mrcbT16-g |
1.8 |
| mrcbT16-h |
3 |
| mrcbT16-i |
0.0031 |
| mrcbT16-j |
1.077 |
| mrcbT16-k |
3429 |
| mrcbT16-q |
57 |
| mrcbT16-s |
1.287 |
| mrcbT16-y |
78.53 |
| mrcbT16-x |
9.16 |
| mrcbT16-3 |
2884 |
| mrcbT16-4 |
Q1 |
| mrcbT16-5 |
6.300 |
| mrcbT16-6 |
143 |
| mrcbT16-7 |
Q1 |
| mrcbT16-C |
97.9 |
| mrcbT16-M |
2.49 |
| mrcbT16-N |
Q1 |
| mrcbT16-P |
99.3 |
| arlyear |
2026 |
| mrcbU14 |
105027415372 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001661114100003 WOS |
| mrcbU63 |
cav_un_epca*0525779 Financial Innovation Roč. 12 2026 2199-4730 Springer |
|