bibtype J - Journal Article
ARLID 0434203
utime 20240103204932.2
mtime 20150129235959.9
SCOPUS 84936890267
WOS 000357669600001
DOI 10.1080/14697688.2014.950319
title (primary) (eng) Realized wavelet-based estimation of integrated variance and jumps in the presence of noise
specification
page_count 18 s.
media_type P
serial
ARLID cav_un_epca*0039898
ISSN 1469-7688
title Quantitative Finance
volume_id 15
volume 8 (2015)
page_num 1347-1364
keyword quadratic variation
keyword realized variance
keyword jumps
keyword market microstructure noise
keyword wavelets
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 K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101217
full_dept (cz) Ekonometrie
full_dept Department of Econometrics
department (cz) E
department E
full_dept Department of Econometrics
share 50
name1 Vácha
name2 Lukáš
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2014/E/barunik-0434203.pdf
cas_special
project
ARLID cav_un_auth*0308905
project_id 612955
agency EC
project
ARLID cav_un_auth*0308909
project_id GA13-24313S
agency GA ČR
country CZ
project
ARLID cav_un_auth*0292677
project_id GA13-32263S
agency GA ČR
abstract (eng) We introduce wavelet-based methodology for estimation of realized variance allowing its mea- surement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Dis- crete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and jumps. Basing our estimator in the two-scale realized variance frame- work, we are able to utilize all available data and get feasible estimator in the presence of microstructure noise as well. The estimator is tested in a large numerical study of the finite sample performance and is compared to other popular realized variation estimators. We use different simulation settings with changing noise as well as jump level in different price pro- cesses including long memory fractional stochastic volatility model. The results reveal that our wavelet-based estimator is able to estimate and forecast the realized measures with the greatest precision.
RIV AH
reportyear 2016
num_of_auth 2
mrcbC52 4 A 4a 20231122140548.4
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0238359
confidential S
mrcbC83 RIV/67985556:_____/15:00434203!RIV16-AV0-67985556 191683078 nejednotny pocet stran UTIA-B
mrcbC83 RIV/67985556:_____/15:00434203!RIV16-GA0-67985556 191719000 nejednotny pocet stran UTIA-B
mrcbT16-e BUSINESSFINANCE|ECONOMICS|MATHEMATICSINTERDISCIPLINARYAPPLICATIONS|SOCIALSCIENCESMATHEMATICALMETHODS
mrcbT16-j 0.633
mrcbT16-s 0.603
mrcbT16-4 Q1
mrcbT16-B 43.64
mrcbT16-C 37.771
mrcbT16-D Q3
mrcbT16-E Q2
arlyear 2015
mrcbTft \nSoubory v repozitáři: barunik-0434203.pdf
mrcbU14 84936890267 SCOPUS
mrcbU34 000357669600001 WOS
mrcbU63 cav_un_epca*0039898 Quantitative Finance 1469-7688 1469-7696 Roč. 15 č. 8 2015 1347 1364