{"id":"https://openalex.org/W7204454264","doi":"https://doi.org/10.33003/fjs-2026-1014-5487","title":"Hybrid Machine Learning-Bayesian Vector Error Correction Model for Forecasting of Exchange Rate and Inflation Volatility Dynamics in Nigeria","display_name":"Hybrid Machine Learning-Bayesian Vector Error Correction Model for Forecasting of Exchange Rate and Inflation Volatility Dynamics in Nigeria","publication_year":2026,"publication_date":"2026-08-27","ids":{"openalex":"https://openalex.org/W7204454264","doi":"https://doi.org/10.33003/fjs-2026-1014-5487"},"language":"en","primary_location":{"id":"doi:10.33003/fjs-2026-1014-5487","doi":"https://doi.org/10.33003/fjs-2026-1014-5487","is_oa":true,"landing_page_url":"https://doi.org/10.33003/fjs-2026-1014-5487","pdf_url":"https://fjs.fudutsinma.edu.ng/index.php/fjs/article/download/5487/3813","source":{"id":"https://openalex.org/S4210224159","display_name":"FUDMA Journal of Sciences","issn_l":"2616-1370","issn":["2616-1370","2645-2944"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4404662838","host_organization_name":"Federal University, Dutsin-Ma","host_organization_lineage":["https://openalex.org/P4404662838"],"host_organization_lineage_names":["Federal University, Dutsin-Ma"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"FUDMA Journal of Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://fjs.fudutsinma.edu.ng/index.php/fjs/article/download/5487/3813","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5147239360","display_name":"Joseph Elekhekhatse Alemho","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joseph Elekhekhatse Alemho","raw_affiliation_strings":["University on the Niger"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University on the Niger","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148442405","display_name":"Abdullahi Damisa Jemilu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdullahi Damisa Jemilu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5095789902","display_name":"Vincent Abiodun Micheal","orcid":"https://orcid.org/0000-0002-7967-8759","observed_orcids":["https://orcid.org/0000-0002-7967-8759"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vincent Abiodun Micheal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"14","first_page":"325","last_page":"333"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9570000171661377,"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"}},"topics":[{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9570000171661377,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.02969999983906746,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.008799999952316284,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/exchange-rate-forecasting","display_name":"exchange rate forecasting","score":0.9269999861717224},{"id":"https://openalex.org/keywords/inflation-forecasting","display_name":"inflation forecasting","score":0.9259999990463257},{"id":"https://openalex.org/keywords/hybrid-machine-learning","display_name":"hybrid machine learning","score":0.5690000057220459},{"id":"https://openalex.org/keywords/exchange-rate-pass-through","display_name":"exchange rate pass-through","score":0.8560000061988831},{"id":"https://openalex.org/keywords/nigeria","display_name":"Nigeria","score":0.5080000162124634},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7730000019073486},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.9990000128746033},{"id":"https://openalex.org/keywords/volatility-clustering","display_name":"volatility clustering","score":0.722000002861023},{"id":"https://openalex.org/keywords/structural-shocks","display_name":"structural shocks","score":0.5139999985694885},{"id":"https://openalex.org/keywords/monetary-policy","display_name":"monetary policy","score":0.5569999814033508}],"concepts":[{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.8377000093460083},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6797999739646912},{"id":"https://openalex.org/C134024062","wikidata":"https://www.wikidata.org/wiki/Q1401229","display_name":"Error correction model","level":3,"score":0.6367999911308289},{"id":"https://openalex.org/C195561663","wikidata":"https://www.wikidata.org/wiki/Q1307899","display_name":"Unit root","level":2,"score":0.6123999953269958},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5658000111579895},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.5512999892234802},{"id":"https://openalex.org/C145162277","wikidata":"https://www.wikidata.org/wiki/Q1751675","display_name":"Cointegration","level":2,"score":0.5292999744415283},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C536366893","wikidata":"https://www.wikidata.org/wiki/Q66076","display_name":"Foreign exchange market","level":3,"score":0.4368000030517578},{"id":"https://openalex.org/C200941418","wikidata":"https://www.wikidata.org/wiki/Q273508","display_name":"Inflation (cosmology)","level":2,"score":0.43320000171661377},{"id":"https://openalex.org/C185824701","wikidata":"https://www.wikidata.org/wiki/Q1193759","display_name":"Inflation targeting","level":3,"score":0.4255000054836273},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C126285488","wikidata":"https://www.wikidata.org/wiki/Q178476","display_name":"Monetary policy","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C2778908629","wikidata":"https://www.wikidata.org/wiki/Q4391528","display_name":"Effective exchange rate","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C186008389","wikidata":"https://www.wikidata.org/wiki/Q7625010","display_name":"Structural break","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C174303752","wikidata":"https://www.wikidata.org/wiki/Q3624438","display_name":"Forecast error","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C2992735868","wikidata":"https://www.wikidata.org/wiki/Q66344","display_name":"Central bank","level":3,"score":0.2858000099658966},{"id":"https://openalex.org/C82843974","wikidata":"https://www.wikidata.org/wiki/Q205231","display_name":"Interest rate parity","level":3,"score":0.2770000100135803},{"id":"https://openalex.org/C2779513878","wikidata":"https://www.wikidata.org/wiki/Q631916","display_name":"Output gap","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C137703641","wikidata":"https://www.wikidata.org/wiki/Q4874480","display_name":"Bayesian vector autoregression","level":3,"score":0.2736000120639801},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C133029050","wikidata":"https://www.wikidata.org/wiki/Q385593","display_name":"Vector autoregression","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C151541453","wikidata":"https://www.wikidata.org/wiki/Q523022","display_name":"Risk premium","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.33003/fjs-2026-1014-5487","doi":"https://doi.org/10.33003/fjs-2026-1014-5487","is_oa":true,"landing_page_url":"https://doi.org/10.33003/fjs-2026-1014-5487","pdf_url":"https://fjs.fudutsinma.edu.ng/index.php/fjs/article/download/5487/3813","source":{"id":"https://openalex.org/S4210224159","display_name":"FUDMA Journal of Sciences","issn_l":"2616-1370","issn":["2616-1370","2645-2944"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4404662838","host_organization_name":"Federal University, Dutsin-Ma","host_organization_lineage":["https://openalex.org/P4404662838"],"host_organization_lineage_names":["Federal University, Dutsin-Ma"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"FUDMA Journal of Sciences","raw_type":"journal-article"},{"id":"pmh:oai:fjs.fudutsinma.edu.ng:article/5487","doi":null,"is_oa":true,"landing_page_url":"https://fjs.fudutsinma.edu.ng/index.php/index/article/view/5487","pdf_url":null,"source":{"id":"https://openalex.org/S4210224159","display_name":"FUDMA Journal of Sciences","issn_l":"2616-1370","issn":["2616-1370","2645-2944"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4404662838","host_organization_name":"Federal University, Dutsin-Ma","host_organization_lineage":["https://openalex.org/P4404662838"],"host_organization_lineage_names":["Federal University, Dutsin-Ma"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2645-2944","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.33003/fjs-2026-1014-5487","doi":"https://doi.org/10.33003/fjs-2026-1014-5487","is_oa":true,"landing_page_url":"https://doi.org/10.33003/fjs-2026-1014-5487","pdf_url":"https://fjs.fudutsinma.edu.ng/index.php/fjs/article/download/5487/3813","source":{"id":"https://openalex.org/S4210224159","display_name":"FUDMA Journal of Sciences","issn_l":"2616-1370","issn":["2616-1370","2645-2944"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4404662838","host_organization_name":"Federal University, Dutsin-Ma","host_organization_lineage":["https://openalex.org/P4404662838"],"host_organization_lineage_names":["Federal University, Dutsin-Ma"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"FUDMA Journal of Sciences","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204454264.pdf","grobid_xml":"https://content.openalex.org/works/W7204454264.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W2034707435","https://openalex.org/W3007621132","https://openalex.org/W7203554550"],"related_works":["https://openalex.org/W7167581747","https://openalex.org/W4410542053","https://openalex.org/W2946160529","https://openalex.org/W7170171171","https://openalex.org/W4415774544","https://openalex.org/W4308114780","https://openalex.org/W7220852491","https://openalex.org/W4415306950","https://openalex.org/W4287906013","https://openalex.org/W7214485018"],"abstract_inverted_index":{"This":[0],"study":[1],"develops":[2],"and":[3,18,37,43,116,142,152,164,202,210,224,232],"evaluates":[4],"a":[5,59,77,86,96,106,215],"hybrid":[6,173,186],"Machine":[7],"Learning-Bayesian":[8],"Vector":[9],"Error":[10],"Correction":[11],"Model":[12],"(ML-BVECM)":[13],"for":[14,114,118,179,190,218],"forecasting":[15,180],"exchange":[16,46,79,92,120,140,191],"rate":[17,47,80,93,121,192],"inflation":[19,42,115,231],"volatility":[20,153],"dynamics":[21],"in":[22,89,95,99],"Nigeria":[23,223],"using":[24,160],"quarterly":[25,108],"data":[26],"from":[27],"1990":[28],"to":[29,82,111,228],"2025.":[30],"The":[31,68,101,212],"Unit":[32],"root":[33],"tests":[34,204],"(ADF,":[35],"PP,":[36],"KPSS)":[38],"reveal":[39],"that":[40],"both":[41],"the":[44,65,72,74,90,119,123,129,137,143,155,170,176,183,206,219],"nominal":[45],"are":[48],"integrated":[49],"of":[50,71,125,222],"order":[51],"one":[52],"(I(1)),":[53],"while":[54,122],"Johansen":[55],"cointegration":[56],"analysis":[57],"confirms":[58],"stable":[60],"long-run":[61],"equilibrium":[62],"relationship":[63],"between":[64],"two":[66],"variables.":[67],"structural":[69,133,208],"component":[70],"model,":[73],"BVECM,":[75],"reveals":[76],"strong":[78],"pass-through":[81],"domestic":[83],"prices,":[84],"where":[85],"1%":[87],"increase":[88],"Naira-Dollar":[91],"results":[94],"0.62%":[97],"rise":[98],"inflation.":[100],"error":[102,196],"correction":[103],"coefficients":[104],"highlight":[105],"rapid":[107],"adjustment":[109],"back":[110],"equilibrium:":[112],"31%":[113],"28%":[117],"use":[124],"Bayesian":[126],"priors":[127],"stabilizes":[128],"parameters":[130],"against":[131],"extreme":[132],"shocks":[134],"such":[135],"as":[136],"2016":[138],"foreign":[139],"crisis":[141],"2023":[144],"currency":[145],"float.":[146],"To":[147],"capture":[148],"remaining":[149],"short-term":[150],"nonlinearities":[151],"clustering,":[154],"BVECM":[156],"residuals":[157],"were":[158],"modeled":[159],"Random":[161,171],"Forest":[162,172],"(RF)":[163],"Gradient":[165,184],"Boosting":[166,185],"(GB)":[167],"algorithms.":[168],"Specifically,":[169],"framework":[174],"yields":[175],"highest":[177],"accuracy":[178],"inflation,":[181],"whereas":[182],"variant":[187],"is":[188],"superior":[189],"predictions,":[193],"driving":[194],"overall":[195],"reductions":[197],"by":[198],"over":[199],"20%.":[200],"Diagnostic":[201],"stability":[203],"confirm":[205],"model's":[207],"integrity":[209],"robustness.":[211],"findings":[213],"offer":[214],"potent":[216],"toolkit":[217],"Central":[220],"Bank":[221],"other":[225],"policy":[226],"makers":[227],"proactively":[229],"target":[230],"navigate":[233],"market":[234],"volatility.":[235]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-08-28T00:00:00"}
