Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities.
Identifieur interne : 001156 ( Main/Exploration ); précédent : 001155; suivant : 001157Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities.
Auteurs : Jules Sadefo Kamdem [France] ; Rose Bandolo Essomba [Cameroun] ; James Njong Berinyuy [Cameroun]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices.
DOI: 10.1016/j.chaos.2020.110215
PubMed: 32839644
PubMed Central: PMC7437517
Affiliations:
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Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32839644</PMID><DateRevised><Year>2020</Year><Month>08</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>140</Volume><PubDate><Year>2020</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities.</ArticleTitle><Pagination><MedlinePgn>110215</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.110215</ELocationID><Abstract><AbstractText>Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sadefo Kamdem</LastName><ForeName>Jules</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>University of Montpellier (MRE EA 7491 et UFR d'Economie) Site Richter Avenue Raymond Dugrand CS 7960634960 MONTPELLIER Cedex 2, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bandolo Essomba</LastName><ForeName>Rose</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>AIMS Cameroon, Cameroon.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Njong Berinyuy</LastName><ForeName>James</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>AIMS Cameroon, Cameroon.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>19</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">ARIMA-Wavelet</Keyword><Keyword MajorTopicYN="N">Commodity market volatility</Keyword><Keyword MajorTopicYN="N">Covid-19 spread</Keyword><Keyword MajorTopicYN="N">Deep learning</Keyword><Keyword MajorTopicYN="N">Forecasting</Keyword><Keyword MajorTopicYN="N">LSTM</Keyword><Keyword MajorTopicYN="N">Pandemics risks</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>07</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>08</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32839644</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.110215</ArticleId><ArticleId IdType="pii">S0960-0779(20)30611-1</ArticleId><ArticleId IdType="pii">110215</ArticleId><ArticleId IdType="pmc">PMC7437517</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
<ReferenceList><Reference><Citation>PLoS One. 2016 May 19;11(5):e0155133</Citation><ArticleIdList><ArticleId IdType="pubmed">27196055</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2017 Jul 14;12(7):e0180944</Citation><ArticleIdList><ArticleId IdType="pubmed">28708865</ArticleId></ArticleIdList></Reference><Reference><Citation>Chaos Solitons Fractals. 2020 Jun;135:109850</Citation><ArticleIdList><ArticleId IdType="pubmed">32355424</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Cameroun</li><li>France</li></country></list><tree><country name="France"><noRegion><name sortKey="Sadefo Kamdem, Jules" sort="Sadefo Kamdem, Jules" uniqKey="Sadefo Kamdem J" first="Jules" last="Sadefo Kamdem">Jules Sadefo Kamdem</name></noRegion></country><country name="Cameroun"><noRegion><name sortKey="Bandolo Essomba, Rose" sort="Bandolo Essomba, Rose" uniqKey="Bandolo Essomba R" first="Rose" last="Bandolo Essomba">Rose Bandolo Essomba</name></noRegion><name sortKey="Njong Berinyuy, James" sort="Njong Berinyuy, James" uniqKey="Njong Berinyuy J" first="James" last="Njong Berinyuy">James Njong Berinyuy</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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