bibtype	J - Journal Article
ARLID	0478480
utime	20240103214544.4
mtime	20170925235959.9
SCOPUS	85021440941
WOS	000411276100002
DOI	10.1515/snde-2016-0101
title (primary) (eng)	Estimation of long memory in volatility using wavelets
specification	page_count 22 s. media_type P
serial	ARLID cav_un_epca*0255788 ISSN 1081-1826 title Studies in Nonlinear Dynamics and Econometrics volume_id 21
keyword	long memory
keyword	wavelets
keyword	whittle
author (primary)	ARLID cav_un_auth*0344060 name1 Kraicová name2 Lucie full_dept (cz) Ekonometrie full_dept (eng) Department of Econometrics department (cz) E department (eng) E institution UTIA-B 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-0478480.pdf
cas_special	project ARLID cav_un_auth*0292677 project_id GA13-32263S agency GA ČR project ARLID cav_un_auth*0308905 project_id 612955 agency EC abstract (eng) This work studies wavelet-based Whittle estimator of the fractionally integrated exponential gen- eralized autoregressive conditional heteroscedasticity (FIEGARCH) model often used for modeling long memory in volatility of financial assets. The newly proposed estimator approximates the spectral density using wavelet transform, which makes it more robust to certain types of irregularities in data. Based on an extensive Monte Carlo study, both behavior of the proposed estimator and its relative performance with respect to traditional estimators are assessed. In addition, we study properties of the estimators in presence of jumps, which brings interesting discussion. We find that wavelet-based estimator may become an attrac- tive robust and fast alternative to the traditional methods of estimation. In particular, a localized version of our estimator becomes attractive in small samples. RIV AH FORD0 50000 FORD1 50200 FORD2 50202 reportyear 2018 num_of_auth 2 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0274595 mrcbC61 1 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 article_num 20160101 mrcbC86 3+4 Article Economics|Social Sciences Mathematical Methods mrcbC86 3+4 Article Economics|Social Sciences Mathematical Methods mrcbC86 3+4 Article Economics|Social Sciences Mathematical Methods mrcbT16-e ECONOMICS|SOCIALSCIENCESMATHEMATICALMETHODS mrcbT16-j 0.302 mrcbT16-s 0.668 mrcbT16-B 18.997 mrcbT16-D Q4 mrcbT16-E Q2 arlyear 2017 mrcbU14 85021440941 SCOPUS mrcbU24 PUBMED mrcbU34 000411276100002 WOS mrcbU63 cav_un_epca*0255788 Studies in Nonlinear Dynamics and Econometrics 1081-1826 1558-3708 Roč. 21 č. 3 2017