bibtype J - Journal Article
ARLID 0478479
utime 20240103214544.3
mtime 20170925235959.9
SCOPUS 84966539553
WOS 000394909900006
DOI 10.1002/for.2423
title (primary) (eng) On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model
specification
page_count 26 s.
media_type P
serial
ARLID cav_un_epca*0251242
ISSN 0277-6693
title Journal of Forecasting
volume_id 36
volume 1 (2017)
page_num 181-206
publisher
name Wiley
keyword Multivariate volatility
keyword realized covariance
keyword portfolio optimisation
author (primary)
ARLID cav_un_auth*0344057
name1 Čech
name2 František
full_dept (cz) Ekonometrie
full_dept (eng) Department of Econometrics
department (cz) E
department (eng) E
institution UTIA-B
full_dept Department of Econometrics
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0242028
name1 Baruník
name2 Jozef
full_dept (cz) Ekonometrie
full_dept Department of Econometrics
department (cz) E
department E
institution UTIA-B
full_dept Department of Econometrics
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2017/E/barunik-0478479.pdf
cas_special
project
ARLID cav_un_auth*0292677
project_id GA13-32263S
agency GA ČR
abstract (eng) Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed gener- alized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade-off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled real- ized covariance estimators deliver further gains compared to realized covariance estimated on a 5-minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used.
RIV AH
FORD0 50000
FORD1 50200
FORD2 50201
reportyear 2018
num_of_auth 2
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0274596
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
confidential S
mrcbC86 1 Article Economics|Management
mrcbC86 1 Article Economics|Management
mrcbC86 1 Article Economics|Management
mrcbT16-e ECONOMICS|MANAGEMENT
mrcbT16-j 0.493
mrcbT16-s 0.792
mrcbT16-B 34.921
mrcbT16-D Q3
mrcbT16-E Q2
arlyear 2017
mrcbU14 84966539553 SCOPUS
mrcbU24 PUBMED
mrcbU34 000394909900006 WOS
mrcbU63 cav_un_epca*0251242 Journal of Forecasting 0277-6693 1099-131X Roč. 36 č. 1 2017 181 206 Wiley