{"id":"https://openalex.org/W4416677832","doi":"https://doi.org/10.1109/dsaa65442.2025.11248015","title":"Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts","display_name":"Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W4416677832","doi":"https://doi.org/10.1109/dsaa65442.2025.11248015"},"language":null,"primary_location":{"id":"doi:10.1109/dsaa65442.2025.11248015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa65442.2025.11248015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038525019","display_name":"Grzegorz Dudek","orcid":"https://orcid.org/0000-0002-2285-0327"},"institutions":[{"id":"https://openalex.org/I130294970","display_name":"Cz\u0119stochowa University of Technology","ror":"https://ror.org/046awyn59","country_code":"PL","type":"education","lineage":["https://openalex.org/I130294970"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Grzegorz Dudek","raw_affiliation_strings":["Czestochowa University of Technology,Faculty of Electrical Engineering,Czestochowa,Poland"],"affiliations":[{"raw_affiliation_string":"Czestochowa University of Technology,Faculty of Electrical Engineering,Czestochowa,Poland","institution_ids":["https://openalex.org/I130294970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036373465","display_name":"Witold Orzeszko","orcid":"https://orcid.org/0000-0001-7473-7775"},"institutions":[{"id":"https://openalex.org/I3019271933","display_name":"Nicolaus Copernicus University","ror":"https://ror.org/0102mm775","country_code":"PL","type":"education","lineage":["https://openalex.org/I3019271933"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Witold Orzeszko","raw_affiliation_strings":["Nicolaus Copernicus University in Toru&#x0144;,Faculty of Economic Sciences and Management,Toru&#x0144;,Poland"],"affiliations":[{"raw_affiliation_string":"Nicolaus Copernicus University in Toru&#x0144;,Faculty of Economic Sciences and Management,Toru&#x0144;,Poland","institution_ids":["https://openalex.org/I3019271933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048259466","display_name":"Piotr Fiszeder","orcid":"https://orcid.org/0000-0002-9777-2239"},"institutions":[{"id":"https://openalex.org/I3019271933","display_name":"Nicolaus Copernicus University","ror":"https://ror.org/0102mm775","country_code":"PL","type":"education","lineage":["https://openalex.org/I3019271933"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Piotr Fiszeder","raw_affiliation_strings":["Nicolaus Copernicus University in Toru&#x0144;,Faculty of Economic Sciences and Management,Toru&#x0144;,Poland"],"affiliations":[{"raw_affiliation_string":"Nicolaus Copernicus University in Toru&#x0144;,Faculty of Economic Sciences and Management,Toru&#x0144;,Poland","institution_ids":["https://openalex.org/I3019271933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5038525019"],"corresponding_institution_ids":["https://openalex.org/I130294970"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.48919484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.7831000089645386,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.7831000089645386,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.0820000022649765,"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":0.025200000032782555,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7669000029563904},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.72079998254776},{"id":"https://openalex.org/keywords/cryptocurrency","display_name":"Cryptocurrency","score":0.6287999749183655},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5497000217437744},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4537999927997589},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4433000087738037},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.43709999322891235},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.39809998869895935},{"id":"https://openalex.org/keywords/consensus-forecast","display_name":"Consensus forecast","score":0.36410000920295715}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7669000029563904},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.72079998254776},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6679999828338623},{"id":"https://openalex.org/C180706569","wikidata":"https://www.wikidata.org/wiki/Q13479982","display_name":"Cryptocurrency","level":2,"score":0.6287999749183655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5942000150680542},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5497000217437744},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4537999927997589},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4433000087738037},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.39809998869895935},{"id":"https://openalex.org/C120954023","wikidata":"https://www.wikidata.org/wiki/Q1127277","display_name":"Consensus forecast","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C60092789","wikidata":"https://www.wikidata.org/wiki/Q7301291","display_name":"Realized variance","level":3,"score":0.3508000075817108},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2903999984264374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2903999984264374},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2549999952316284},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.25220000743865967},{"id":"https://openalex.org/C60782215","wikidata":"https://www.wikidata.org/wiki/Q3333679","display_name":"Probabilistic method","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa65442.2025.11248015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa65442.2025.11248015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6400428329","display_name":null,"funder_award_id":"2021/43/B/HS4/00353,2019/35/B/HS4/00642","funder_id":"https://openalex.org/F4320322511","funder_display_name":"Narodowe Centrum Nauki"}],"funders":[{"id":"https://openalex.org/F4320322511","display_name":"Narodowe Centrum Nauki","ror":"https://ror.org/03ha2q922"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2802435027","https://openalex.org/W2816105620","https://openalex.org/W3007066689","https://openalex.org/W3040901468","https://openalex.org/W3129310709","https://openalex.org/W3199331248","https://openalex.org/W4200370185","https://openalex.org/W4241996101","https://openalex.org/W4389410587","https://openalex.org/W4399551319","https://openalex.org/W4399557663","https://openalex.org/W4400031017","https://openalex.org/W4403582379","https://openalex.org/W4406805207"],"related_works":[],"abstract_inverted_index":{"Cryptocurrency":[0],"markets":[1,111],"are":[2,24],"characterized":[3],"by":[4],"ex-treme":[5],"volatility,":[6],"making":[7],"accurate":[8],"forecasts":[9,54,106],"essential":[10],"for":[11,26,123],"effective":[12],"risk":[13,169],"management":[14],"and":[15,67,102,168],"informed":[16],"trading":[17],"strategies.":[18],"Traditional":[19],"deterministic":[20],"(point)":[21],"forecasting":[22,187],"methods":[23,50],"inadequate":[25],"capturing":[27],"the":[28,36,87,94,98,127,156,159,182],"full":[29],"spectrum":[30],"of":[31,38,59,82,89,107,158],"potential":[32],"volatility":[33,146,173],"outcomes,":[34],"underscoring":[35],"importance":[37],"probabilistic":[39,48,105,160,186],"approaches.":[40],"To":[41,86],"address":[42],"this":[43,45,92],"limitation,":[44],"paper":[46],"introduces":[47],"fore-casting":[49],"that":[51,126],"leverage":[52],"point":[53],"from":[55,116],"a":[56,178],"wide":[57],"range":[58],"base":[60,118,140],"models,":[61],"including":[62],"statistical":[63],"(HAR,":[64],"GARCH,":[65],"ARFIMA)":[66],"machine":[68],"learning":[69],"(e.g.":[70],"LASSO,":[71],"SVR,":[72],"MLP,":[73],"Random":[74],"Forest,":[75],"LSTM)":[76],"algorithms,":[77],"to":[78,100,138,191],"estimate":[79],"conditional":[80],"quantiles":[81],"cryp-tocurrency":[83],"realized":[84,145],"variance.":[85],"best":[88],"our":[90],"knowledge,":[91],"is":[93],"first":[95],"study":[96],"in":[97,109,171,181],"literature":[99],"propose":[101],"systematically":[103],"evaluate":[104],"variance":[108],"cryptocurrency":[110,172,192],"based":[112],"on":[113,143],"predictions":[114],"derived":[115],"multiple":[117],"models.":[119],"Our":[120],"empirical":[121],"results":[122],"Bitcoin":[124],"demonstrate":[125],"Quantile":[128],"Estimation":[129],"through":[130],"Residual":[131],"Simulation":[132],"(QRS)":[133],"method,":[134],"partic-ularly":[135],"when":[136],"applied":[137],"linear":[139],"models":[141],"operating":[142],"log-transformed":[144],"data,":[147],"consistently":[148],"outperforms":[149],"more":[150],"sophisticated":[151],"alternatives.":[152],"Additionally,":[153],"we":[154],"highlight":[155],"robustness":[157],"stacking":[161],"framework,":[162],"providing":[163],"comprehensive":[164],"insights":[165],"into":[166],"uncertainty":[167],"inherent":[170],"forecasting.":[174],"This":[175],"research":[176],"fills":[177],"sig-nificant":[179],"gap":[180],"literature,":[183],"contributing":[184],"practical":[185],"methodologies":[188],"tailored":[189],"specifically":[190],"markets.":[193]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-25T00:00:00"}
