| bibtype |
J -
Journal Article
|
| ARLID |
0456185 |
| utime |
20240103211849.1 |
| mtime |
20160229235959.9 |
| SCOPUS |
84960075958 |
| WOS |
000374811000017 |
| DOI |
10.1016/j.eswa.2016.02.008 |
| title
(primary) (eng) |
Combining high frequency data with non-linear models for forecasting energy market volatility |
| specification |
| page_count |
36 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0252943 |
| ISSN |
0957-4174 |
| title
|
Expert Systems With Applications |
| volume_id |
55 |
| volume |
1 (2016) |
| page_num |
222-242 |
| publisher |
|
|
| keyword |
artificial neural networks |
| keyword |
realized volatility |
| keyword |
multiple-step-ahead forecasts |
| keyword |
energy markets |
| author
(primary) |
| ARLID |
cav_un_auth*0242028 |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| full_dept |
Department of Econometrics |
| share |
50 |
| name1 |
Baruník |
| name2 |
Jozef |
| institution |
UTIA-B |
| garant |
A |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0327877 |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| share |
50 |
| name1 |
Křehlík |
| name2 |
Tomáš |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0281000 |
| project_id |
GBP402/12/G097 |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets' price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two ways. First, to capture the complex patterns hidden in linear models commonly used to forecast realized volatility, we propose a novel framework that couples realized measures with generalized regression based on artificial neural networks. Our second contribution is to comprehensively evaluate multiple-step-ahead volatility forecasts of energy markets using several popular high frequency measures and forecasting models. We compare forecasting performance across models and across realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods: the pre-crisis period, the 2008 global financial crisis, and the post-crisis period. |
| RIV |
AH |
| reportyear |
2017 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122141530.1 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0260445 |
| cooperation |
| ARLID |
cav_un_auth*0308308 |
| name |
Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague |
| institution |
IES FSV UK |
| country |
CZ |
|
| mrcbC64 |
1 Department of Econometrics UTIA-B 50200 OPERATIONS RESEARCH & MANAGEMENT SCIENCE |
| confidential |
S |
| mrcbC86 |
2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic|Operations Research Management Science |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE|ENGINEERINGELECTRICALELECTRONIC|OPERATIONSRESEARCHMANAGEMENTSCIENCE |
| mrcbT16-j |
0.719 |
| mrcbT16-s |
1.343 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
60.017 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2016 |
| mrcbTft |
\nSoubory v repozitáři: barunik-0456185.pdf |
| mrcbU14 |
84960075958 SCOPUS |
| mrcbU34 |
000374811000017 WOS |
| mrcbU63 |
cav_un_epca*0252943 Expert Systems With Applications 0957-4174 1873-6793 Roč. 55 č. 1 2016 222 242 Elsevier |
|