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<bibitem type="J">   <ARLID>0478480</ARLID> <utime>20240103214544.4</utime><mtime>20170925235959.9</mtime>   <SCOPUS>85021440941</SCOPUS> <WOS>000411276100002</WOS>  <DOI>10.1515/snde-2016-0101</DOI>           <title language="eng" primary="1">Estimation of long memory in volatility using wavelets</title>  <specification> <page_count>22 s.</page_count> <media_type>P</media_type> </specification>   <serial><ARLID>cav_un_epca*0255788</ARLID><ISSN>1081-1826</ISSN><title>Studies in Nonlinear Dynamics and Econometrics</title><part_num/><part_title/><volume_id>21</volume_id><volume/></serial>    <keyword>long memory</keyword>   <keyword>wavelets</keyword>   <keyword>whittle</keyword>    <author primary="1"> <ARLID>cav_un_auth*0344060</ARLID> <name1>Kraicová</name1> <name2>Lucie</name2> <full_dept language="cz">Ekonometrie</full_dept> <full_dept language="eng">Department of Econometrics</full_dept> <department language="cz">E</department> <department language="eng">E</department> <institution>UTIA-B</institution> <country>CZ</country> <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author> <author primary="0"> <ARLID>cav_un_auth*0242028</ARLID> <name1>Baruník</name1> <name2>Jozef</name2> <full_dept language="cz">Ekonometrie</full_dept> <full_dept>Department of Econometrics</full_dept> <department language="cz">E</department> <department>E</department> <institution>UTIA-B</institution> <full_dept>Department of Econometrics</full_dept> <country>CZ</country> <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>   <source> <url>http://library.utia.cas.cz/separaty/2017/E/barunik-0478480.pdf</url> </source>        <cas_special> <project> <ARLID>cav_un_auth*0292677</ARLID> <project_id>GA13-32263S</project_id> <agency>GA ČR</agency> </project> <project> <ARLID>cav_un_auth*0308905</ARLID> <project_id>612955</project_id> <agency>EC</agency>   </project>  <abstract language="eng" primary="1">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.</abstract>     <RIV>AH</RIV> <FORD0>50000</FORD0> <FORD1>50200</FORD1> <FORD2>50202</FORD2>    <reportyear>2018</reportyear>      <num_of_auth>2</num_of_auth>  <inst_support> RVO:67985556 </inst_support>  <permalink>http://hdl.handle.net/11104/0274595</permalink>  <unknown tag="mrcbC61"> 1 </unknown> <cooperation> <ARLID>cav_un_auth*0308308</ARLID> <name>Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague</name> <institution>IES FSV UK</institution> <country>CZ</country> </cooperation>  <confidential>S</confidential>  <article_num> 20160101 </article_num> <unknown tag="mrcbC86"> 3+4 Article Economics|Social Sciences Mathematical Methods  </unknown> <unknown tag="mrcbC86"> 3+4 Article Economics|Social Sciences Mathematical Methods  </unknown> <unknown tag="mrcbC86"> 3+4 Article Economics|Social Sciences Mathematical Methods  </unknown>         <unknown tag="mrcbT16-e">ECONOMICS|SOCIALSCIENCES.MATHEMATICALMETHODS</unknown> <unknown tag="mrcbT16-f">0.734</unknown> <unknown tag="mrcbT16-g">0.115</unknown> <unknown tag="mrcbT16-h">11.1</unknown> <unknown tag="mrcbT16-i">0.00064</unknown> <unknown tag="mrcbT16-j">0.302</unknown> <unknown tag="mrcbT16-k">401</unknown> <unknown tag="mrcbT16-s">0.668</unknown> <unknown tag="mrcbT16-5">0.823</unknown> <unknown tag="mrcbT16-6">26</unknown> <unknown tag="mrcbT16-7">Q3</unknown> <unknown tag="mrcbT16-B">18.997</unknown> <unknown tag="mrcbT16-C">30.8</unknown> <unknown tag="mrcbT16-D">Q4</unknown> <unknown tag="mrcbT16-E">Q2</unknown> <unknown tag="mrcbT16-M">0.35</unknown> <unknown tag="mrcbT16-N">Q3</unknown> <unknown tag="mrcbT16-P">34.136</unknown> <arlyear>2017</arlyear>       <unknown tag="mrcbU14"> 85021440941 SCOPUS </unknown> <unknown tag="mrcbU24"> PUBMED </unknown> <unknown tag="mrcbU34"> 000411276100002 WOS </unknown> <unknown tag="mrcbU63"> cav_un_epca*0255788 Studies in Nonlinear Dynamics and Econometrics 1081-1826 1558-3708 Roč. 21 č. 3 2017 </unknown> </cas_special> </bibitem>