Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets
A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation anal...
Saved in:
Published in | Chaos (Woodbury, N.Y.) Vol. 27; no. 6; p. 063111 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
01.06.2017
|
Online Access | Get more information |
Cover
Loading…
Abstract | A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series. |
---|---|
AbstractList | A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series. |
Author | Anh, Vo Wei, Yun-Lan Yu, Zu-Guo Zou, Hai-Long |
Author_xml | – sequence: 1 givenname: Yun-Lan surname: Wei fullname: Wei, Yun-Lan organization: Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, China – sequence: 2 givenname: Zu-Guo surname: Yu fullname: Yu, Zu-Guo organization: Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, China – sequence: 3 givenname: Hai-Long surname: Zou fullname: Zou, Hai-Long organization: Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, China – sequence: 4 givenname: Vo surname: Anh fullname: Anh, Vo organization: School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane Q4001, Australia |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28679233$$D View this record in MEDLINE/PubMed |
BookMark | eNptUM1KxDAYDKK4P3rwBSQv0DV_TdKjLP7Bihc9L2nyVeumTU2yLH0Nn9jC6s3TDAMzzMwCnfahB4SuKFlRIvkNXYlKl5KrEzSnRFeFkprN0CKlT0IIZbw8RzOmpaoY53P0_bz3uW2isdl4nKEbQjTej_gA7ftHBocd5Ai9m5iNIaXChhjBm9yGHpve-DG1CeeAv_amn5JGPIQDxMKbw78Gh9ucsBkG39qjNnlTDnaHOxN3kNMFOmuMT3D5i0v0dn_3un4sNi8PT-vbTWG55rnQmpQUKK-UVYJPUw0wW6oanOCE1VoYqaUWmolGWq2FdApKWgthRaNZWbMluj7mDvu6A7cdYjs1GLd_57Af-dVpDA |
CitedBy_id | crossref_primary_10_1142_S0218348X23500895 crossref_primary_10_1142_S0219477523500463 crossref_primary_10_1016_j_cnsns_2020_105579 crossref_primary_10_1016_j_physa_2019_01_125 crossref_primary_10_1016_j_chaos_2018_02_028 crossref_primary_10_1088_1361_6633_ab42fb crossref_primary_10_1016_j_physa_2017_12_009 crossref_primary_10_1007_s11071_018_4241_y crossref_primary_10_1016_j_cnsns_2021_105781 crossref_primary_10_1142_S021947752050011X crossref_primary_10_1016_j_physa_2022_127627 crossref_primary_10_1038_s41598_018_25822_w crossref_primary_10_1063_1_5129574 crossref_primary_10_1016_j_chaos_2020_109914 crossref_primary_10_1016_j_physa_2021_125920 crossref_primary_10_3390_e22091003 crossref_primary_10_1142_S0218348X21501668 crossref_primary_10_1016_j_chaos_2024_114674 crossref_primary_10_1038_s41598_018_38215_w crossref_primary_10_1142_S0219477522500353 crossref_primary_10_1016_j_physa_2019_121086 |
ContentType | Journal Article |
DBID | NPM |
DOI | 10.1063/1.4985637 |
DatabaseName | PubMed |
DatabaseTitle | PubMed |
DatabaseTitleList | PubMed |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
DeliveryMethod | no_fulltext_linktorsrc |
Discipline | Sciences (General) Physics |
EISSN | 1089-7682 |
ExternalDocumentID | 28679233 |
Genre | Journal Article |
GroupedDBID | --- -~X .DC 0ZJ 1UP 2-P 29B 4.4 53G 5VS 6TJ 8WZ A6W AAAAW AABDS AAEUA AAPUP AAYIH ABJNI ACBRY ACGFS ACLYJ ACNCT ACZLF ADCTM AEJMO AENEX AFATG AFFNX AFHCQ AGKCL AGLKD AGMXG AGTJO AHSDT AJJCW AJQPL ALEPV ALMA_UNASSIGNED_HOLDINGS AQWKA ATXIE AWQPM BDMKI BPZLN DU5 EBS EJD ESX F5P FDOHQ FFFMQ HAM M6X M71 M73 N9A NEUPN NPM NPSNA O-B OHT P2P RDFOP RIP RNS ROL RQS TAE WH7 WHG |
ID | FETCH-LOGICAL-c383t-88051e1397c743108ae2c57bed4302b84a68684824f6c8846d7e51b44c4f825b2 |
IngestDate | Sat Sep 28 08:45:57 EDT 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 6 |
Language | English |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-c383t-88051e1397c743108ae2c57bed4302b84a68684824f6c8846d7e51b44c4f825b2 |
PMID | 28679233 |
ParticipantIDs | pubmed_primary_28679233 |
PublicationCentury | 2000 |
PublicationDate | 2017-Jun |
PublicationDateYYYYMMDD | 2017-06-01 |
PublicationDate_xml | – month: 06 year: 2017 text: 2017-Jun |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States |
PublicationTitle | Chaos (Woodbury, N.Y.) |
PublicationTitleAlternate | Chaos |
PublicationYear | 2017 |
SSID | ssj0001235 |
Score | 2.3438258 |
Snippet | A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in... |
SourceID | pubmed |
SourceType | Index Database |
StartPage | 063111 |
Title | Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets |
URI | https://www.ncbi.nlm.nih.gov/pubmed/28679233 |
Volume | 27 |
hasFullText | |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELa2IKReKlreL_nAARR5ySaO4xxRBaxQ6amF9lT5FRWxXW_ZRFX5GfwL_iXjR7KhXRBwiVa2vFplvh2Pv5n5jNDzVCjYtXVNYPN1aUZGiSi1IAz-5KbgFSulowY-7LPpIX1_VByNRj8GVUttI8fq29q-kv-xKoyBXV2X7D9Ytv9SGIDPYF94goXh-Vc29t2ztetzciXjQWRqNrtMLjzfCaGkNs1Xz3Enfjckyt3FEarfEtHJkUD0ed4KVzR0mSzcpWlkJi7WLghphkHO262F6FF9Sc589_RyGOvungrrOd1P1vb5-r6zq0sI-WqC43ZO9lY4PW59zqQl71rbM9vWD07FZ7Jn43br6QvPC320Q_piUq7KrMYmuNyUVwQOPb_45KAXELE3dLAQUUXvfM33w5SjIca04gULSjIDDCzOPAgyJzCYBe2NP89ekeHupjbQRsmdK913tNBmR-blRSdbxfJX_W9wUtNx3ZVjiw9fDm6jrXjuwK8DiLbRyMx30C1f_6uWO2g7-vglfhGFyF_eQd-H-MIrfOEOX7jHF74GF9zhCzcWd_jCPb7WLtAY8IUH-HJrPb5wxNdddPj2zcHulMQ7PIjKed4Q2B6KiXHHDOVi1ZQLk6milEbTPM0kp4JxxinPaM0Uh2BYl6aYSEoVrXlWyOweujG3c_MAYVppF33nOpU5rSSvUq7NpNaKmaLSIn2I7oc3fLIIQi0n3bt_9NuZx2hzBcon6GYNnsE8hTCzkc-8iX8CixWCew |
link.rule.ids | 786 |
linkProvider | National Library of Medicine |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multifractal+temporally+weighted+detrended+cross-correlation+analysis+to+quantify+power-law+cross-correlation+and+its+application+to+stock+markets&rft.jtitle=Chaos+%28Woodbury%2C+N.Y.%29&rft.au=Wei%2C+Yun-Lan&rft.au=Yu%2C+Zu-Guo&rft.au=Zou%2C+Hai-Long&rft.au=Anh%2C+Vo&rft.date=2017-06-01&rft.eissn=1089-7682&rft.volume=27&rft.issue=6&rft.spage=063111&rft_id=info:doi/10.1063%2F1.4985637&rft_id=info%3Apmid%2F28679233&rft_id=info%3Apmid%2F28679233&rft.externalDocID=28679233 |