{"id":"https://openalex.org/W2854999190","doi":"https://doi.org/10.1109/access.2018.2853998","title":"A Bayesian Learning Method for Financial Time-Series Analysis","display_name":"A Bayesian Learning Method for Financial Time-Series Analysis","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2854999190","doi":"https://doi.org/10.1109/access.2018.2853998","mag":"2854999190"},"language":"en","primary_location":{"id":"doi:10.1109/access.2018.2853998","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2853998","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2018.2853998","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109400524","display_name":"Fumin Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fumin Zhu","raw_affiliation_strings":["College of Economics, Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-8181-0958","affiliations":[{"raw_affiliation_string":"College of Economics, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036159136","display_name":"Wei Quan","orcid":"https://orcid.org/0000-0001-7454-0905"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Quan","raw_affiliation_strings":["College of Economics, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062570174","display_name":"Zunxin Zheng","orcid":"https://orcid.org/0000-0001-6718-7672"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zunxin Zheng","raw_affiliation_strings":["College of Economics, Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-6718-7672","affiliations":[{"raw_affiliation_string":"College of Economics, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003203521","display_name":"Shaohua Wan","orcid":"https://orcid.org/0000-0001-7013-9081"},"institutions":[{"id":"https://openalex.org/I158934434","display_name":"Zhongnan University of Economics and Law","ror":"https://ror.org/04yqxxq63","country_code":"CN","type":"education","lineage":["https://openalex.org/I158934434"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohua Wan","raw_affiliation_strings":["School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7013-9081","affiliations":[{"raw_affiliation_string":"School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China","institution_ids":["https://openalex.org/I158934434"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.4574,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92467453,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"6","issue":null,"first_page":"38959","last_page":"38966"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.6698789596557617},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6229768991470337},{"id":"https://openalex.org/keywords/autoregressive-conditional-heteroskedasticity","display_name":"Autoregressive conditional heteroskedasticity","score":0.586392879486084},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5649614930152893},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5389310717582703},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5227918028831482},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.48644518852233887},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.48384395241737366},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.46001455187797546},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44982168078422546},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4300227761268616},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4262143075466156},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.386080801486969},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3846701383590698},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3594987392425537},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3583342432975769},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.283638596534729},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.11527076363563538}],"concepts":[{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.6698789596557617},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6229768991470337},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.586392879486084},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5649614930152893},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5389310717582703},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5227918028831482},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.48644518852233887},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.48384395241737366},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.46001455187797546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44982168078422546},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4300227761268616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4262143075466156},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.386080801486969},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3846701383590698},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3594987392425537},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3583342432975769},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.283638596534729},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.11527076363563538},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2018.2853998","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2853998","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:92c2aa66558a485ba916b3c3264f8eaa","is_oa":true,"landing_page_url":"https://doaj.org/article/92c2aa66558a485ba916b3c3264f8eaa","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 6, Pp 38959-38966 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2018.2853998","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2853998","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4217466762","display_name":"\u57fa\u4e8e\u8c03\u548c\u7a33\u6001Levy\u8fc7\u7a0b\u7684\u8df3-\u6269\u6563\u53cc\u56e0\u5b50\u4ea4\u53c9\u56de\u9988\u6a21\u578b\u7684\u671f\u6743\u5b9a\u4ef7\u7814\u7a76","funder_award_id":"71601125","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5995226476","display_name":"\u57fa\u4e8e\u5546\u54c1\u4ef7\u683c\u8054\u52a8\u89c6\u89d2\u7684\u591a\u5546\u54c1\u671f\u8d27\u5b9a\u4ef7\u7814\u7a76:\u4e2d\u56fd\u5e02\u573a\u7684\u5b9e\u8bc1","funder_award_id":"71471119","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325571","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70"},{"id":"https://openalex.org/F4320331904","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W116417043","https://openalex.org/W124040795","https://openalex.org/W1501586228","https://openalex.org/W1518940614","https://openalex.org/W1593484081","https://openalex.org/W1876120984","https://openalex.org/W1909638435","https://openalex.org/W1979575715","https://openalex.org/W1981364618","https://openalex.org/W1998841711","https://openalex.org/W1999674105","https://openalex.org/W1999996900","https://openalex.org/W2031817855","https://openalex.org/W2037619382","https://openalex.org/W2046811824","https://openalex.org/W2048515311","https://openalex.org/W2050373719","https://openalex.org/W2055410770","https://openalex.org/W2077039755","https://openalex.org/W2079223104","https://openalex.org/W2080017901","https://openalex.org/W2098613108","https://openalex.org/W2099544223","https://openalex.org/W2109375023","https://openalex.org/W2113926057","https://openalex.org/W2116632544","https://openalex.org/W2117979508","https://openalex.org/W2124156864","https://openalex.org/W2131598171","https://openalex.org/W2133543800","https://openalex.org/W2144246192","https://openalex.org/W2160337655","https://openalex.org/W2329976112","https://openalex.org/W2354306969","https://openalex.org/W2423293082","https://openalex.org/W2734811486","https://openalex.org/W2747423797","https://openalex.org/W2792441096","https://openalex.org/W2964101532","https://openalex.org/W2964333607","https://openalex.org/W3104382201","https://openalex.org/W3121154744","https://openalex.org/W3121793849","https://openalex.org/W3121918541","https://openalex.org/W3122580191","https://openalex.org/W3122832813","https://openalex.org/W3123118666","https://openalex.org/W3123549399","https://openalex.org/W3124597381","https://openalex.org/W3143394208","https://openalex.org/W4244486013","https://openalex.org/W6604408473","https://openalex.org/W6635525629"],"related_works":["https://openalex.org/W1608601224","https://openalex.org/W2126907425","https://openalex.org/W2208639223","https://openalex.org/W2017138702","https://openalex.org/W2114656557","https://openalex.org/W2195963939","https://openalex.org/W1978494725","https://openalex.org/W1499764293","https://openalex.org/W2514372983","https://openalex.org/W2776656900"],"abstract_inverted_index":{"This":[0,33],"article":[1],"develops":[2],"a":[3,40],"sequential":[4,66,91,122],"Bayesian":[5,67,123],"learning":[6,68,93,124],"method":[7,35,125],"to":[8,118],"estimate":[9],"the":[10,14,29,47,52,62,65,71,90,101,107,110,121],"parameters":[11],"and":[12,51,98],"recover":[13],"state":[15],"variables":[16,56],"for":[17,129],"generalized":[18],"autoregressive":[19],"conditional":[20],"heteroscedasticity":[21],"(GARCH)":[22],"models,":[23],"which":[24],"are":[25],"commonly":[26],"used":[27],"in":[28,77],"financial":[30],"time-series":[31],"analysis.":[32],"simulation-based":[34],"combines":[36],"particle-filtering":[37],"technology":[38],"with":[39,70],"Markov":[41],"chain":[42],"Monte":[43],"Carlo":[44],"algorithm":[45],"when":[46],"model":[48],"is":[49,57,126],"non-linear":[50],"number":[53],"of":[54,64,109],"observed":[55],"relatively":[58],"sparse.":[59],"We":[60,112],"compare":[61],"performance":[63],"approach":[69,94],"numerical":[72],"maximum":[73],"likelihood":[74],"estimation":[75],"(NMLE)":[76],"estimating":[78],"models":[79],"based":[80],"on":[81],"S&P":[82],"500":[83],"return":[84],"rates.":[85],"Our":[86],"research":[87],"concludes":[88],"that":[89,120],"parameter":[92],"performs":[95],"more":[96],"robustly":[97],"accurately":[99],"than":[100],"NMLE,":[102],"by":[103],"taking":[104],"into":[105],"account":[106],"uncertainty":[108],"model.":[111],"also":[113],"carry":[114],"out":[115],"simulation":[116],"studies":[117],"confirm":[119],"extremely":[127],"reliable":[128],"GARCH":[130],"models.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}