| bibtype |
V -
Research Report
|
| ARLID |
0577571 |
| utime |
20240402214649.2 |
| mtime |
20231104235959.9 |
| title
(primary) (eng) |
Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness |
| publisher |
| place |
IES UK |
| name |
IES UK |
| pub_time |
2023 |
|
| specification |
| page_count |
28 s. |
| media_type |
E |
|
| edition |
| name |
IES Working Papers |
| volume_id |
24/2023 |
|
| keyword |
Volatility |
| keyword |
Dynamic connectedness |
| keyword |
Asymmetric effects |
| keyword |
Cryptocurrency |
| 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 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| 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 |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0293468 |
| name1 |
Kukačka |
| name2 |
Jiří |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| country |
CZ |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| 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 |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA23-06606S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0458718 |
|
| abstract
(eng) |
Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. The results suggest that crypto connectedness reflects important events and exhibits more variable and cyclical dynamics than those of traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that information about market downturns spills over substantially faster than news about comparable market surges. Overall, the connectedness dynamics are predominantly driven by fundamental crypto factors, while the asymmetry measure also depends on macro factors such as the VIX index and the expected inflation. |
| RIV |
AH |
| FORD0 |
50000 |
| FORD1 |
50200 |
| FORD2 |
50206 |
| reportyear |
2024 |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0347641 |
| confidential |
S |
| arlyear |
2023 |
| mrcbU10 |
2023 |
| mrcbU10 |
IES UK IES UK |
|