{"id":"https://openalex.org/W4226462837","doi":"https://doi.org/10.1145/3501774.3501793","title":"Realized Volatility Prediction","display_name":"Realized Volatility Prediction","publication_year":2021,"publication_date":"2021-11-19","ids":{"openalex":"https://openalex.org/W4226462837","doi":"https://doi.org/10.1145/3501774.3501793"},"language":"en","primary_location":{"id":"doi:10.1145/3501774.3501793","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3501774.3501793","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd European Symposium on Software Engineering","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/A5039895038","display_name":"Sabrina Wing-Yi Chio","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sabrina Wing-Yi Chio","raw_affiliation_strings":["Crofton House School, Canada"],"affiliations":[{"raw_affiliation_string":"Crofton House School, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355229","display_name":"Yifei Li","orcid":"https://orcid.org/0000-0003-3261-8182"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yifei Li","raw_affiliation_strings":["Indian Springs School, USA"],"affiliations":[{"raw_affiliation_string":"Indian Springs School, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061397669","display_name":"Rainie JingRan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rainie JingRan Yang","raw_affiliation_strings":["Crofton House School, Canada"],"affiliations":[{"raw_affiliation_string":"Crofton House School, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5039895038"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4435,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.69561725,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"129","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9986000061035156,"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":0.9986000061035156,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9817000031471252,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5225943326950073},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.48543888330459595},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2640118896961212},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1330142319202423}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5225943326950073},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.48543888330459595},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2640118896961212},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1330142319202423}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3501774.3501793","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3501774.3501793","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd European Symposium on Software Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1968371014","https://openalex.org/W1999996900","https://openalex.org/W2117922789","https://openalex.org/W3094176575","https://openalex.org/W4289552495"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Objective:":[0],"The":[1,81],"purpose":[2],"of":[3,18,67,86,131,148],"this":[4],"paper":[5],"is":[6,43,97],"to":[7,22,56,90],"use":[8],"machine":[9,50,114],"learning":[10,46,51,115,123],"and":[11,35,49,54,61,126,134,143,150,157,166],"time":[12,29,119,138,167],"series":[13,30,120,139,168],"models":[14,31,52,105,116,124,140],"in":[15,88],"the":[16,65,74,84,91,104,113,118],"context":[17],"high":[19],"frequency":[20],"trading":[21],"forecast":[23,159],"stock":[24,70,160],"prices.":[25],"Method:":[26],"We":[27],"analyzed":[28],"such":[32],"as":[33],"ARMA":[34],"GARCH,":[36],"gradient":[37],"boosting":[38],"tree":[39],"model":[40,47],"\u2013":[41,48],"which":[42],"a":[44,94,146],"deep":[45],"FFNN":[53],"GBM":[55,127,158],"compare":[57],"each":[58,68],"models\u2019":[59,69],"benefits":[60],"drawbacks.":[62],"To":[63],"determine":[64],"accuracy":[66],"forecasting,":[71],"we":[72,110],"calculated":[73],"root":[75],"mean":[76],"square":[77],"percentage":[78],"error":[79,87],"(RMSPE).":[80],"RMSPE":[82,130,147],"reveals":[83],"magnitude":[85],"relation":[89],"actual":[92],"values;":[93],"lower":[95],"value":[96],"wanted.":[98],"Results":[99],"&":[100],"Conclusion:":[101],"After":[102],"evaluating":[103],"against":[106],"real":[107],"market":[108],"data,":[109],"found":[111],"that":[112],"outperformed":[117],"models.":[121,169],"Machine":[122],"FFNN,":[125],"have":[128],"an":[129],"roughly":[132],"0.20":[133],"0.21,":[135],"respectively,":[136],"while":[137],"Garch":[141],"1":[142],"2":[144],"had":[145],"0.32":[149],"0.37,":[151],"respectively.":[152],"Therefore,":[153],"feed-forward":[154],"neural":[155],"network":[156],"prices":[161],"more":[162],"accurately":[163],"than":[164],"LSTM":[165],"Unsupervised":[170],"algorithms":[171],"improve":[172],"prediction":[173],"accuracy.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}