A Predictive Model for Oil Market under Uncertainty: Data-Driven System Dynamics Approach
In recent years, there have been a lot of sharp changes in the oil price. These rapid changes cause the traditional models to fail in predicting the price behavior. The main reason for the failure of the traditional models is that they consider the actual value of parameters instead of their expecta...
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creator | Aghaei, Sina Langroudi, Amirreza Safari Fekri, Masoud |
description | In recent years, there have been a lot of sharp changes in the oil price.
These rapid changes cause the traditional models to fail in predicting the
price behavior. The main reason for the failure of the traditional models is
that they consider the actual value of parameters instead of their
expectational ones. In this paper, we propose a system dynamics model that
incorporates expectational variables in determining the oil price. In our
model, the oil price is determined by the expected demand and supply vs. their
actual values. Our core model is based upon regression analysis on several
historic time series and adjusted by adding many casual loops in the oil
market. The proposed model in simulated in different scenarios that have
happened in the past and our results comply with the trends of the oil price in
each of the scenarios. |
doi_str_mv | 10.48550/arxiv.1808.04150 |
format | Article |
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These rapid changes cause the traditional models to fail in predicting the
price behavior. The main reason for the failure of the traditional models is
that they consider the actual value of parameters instead of their
expectational ones. In this paper, we propose a system dynamics model that
incorporates expectational variables in determining the oil price. In our
model, the oil price is determined by the expected demand and supply vs. their
actual values. Our core model is based upon regression analysis on several
historic time series and adjusted by adding many casual loops in the oil
market. The proposed model in simulated in different scenarios that have
happened in the past and our results comply with the trends of the oil price in
each of the scenarios.</description><identifier>DOI: 10.48550/arxiv.1808.04150</identifier><language>eng</language><subject>Quantitative Finance - Economics</subject><creationdate>2018-08</creationdate><rights>http://creativecommons.org/publicdomain/zero/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1808.04150$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1808.04150$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Aghaei, Sina</creatorcontrib><creatorcontrib>Langroudi, Amirreza Safari</creatorcontrib><creatorcontrib>Fekri, Masoud</creatorcontrib><title>A Predictive Model for Oil Market under Uncertainty: Data-Driven System Dynamics Approach</title><description>In recent years, there have been a lot of sharp changes in the oil price.
These rapid changes cause the traditional models to fail in predicting the
price behavior. The main reason for the failure of the traditional models is
that they consider the actual value of parameters instead of their
expectational ones. In this paper, we propose a system dynamics model that
incorporates expectational variables in determining the oil price. In our
model, the oil price is determined by the expected demand and supply vs. their
actual values. Our core model is based upon regression analysis on several
historic time series and adjusted by adding many casual loops in the oil
market. The proposed model in simulated in different scenarios that have
happened in the past and our results comply with the trends of the oil price in
each of the scenarios.</description><subject>Quantitative Finance - Economics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAUhmEvDKhwAUz4BhKOFf-FLWr4k1oViTIwRSfOsbBI0sgxFbl7oDB90_tJD2NXAnJplYIbjF_hmAsLNgcpFJyzt4o_R-qCS-FIfHvoqOf-EPku9HyL8YMS_xw7ivx1dBQThjEtt7zGhFkdf5KRvyxzooHXy4hDcDOvpike0L1fsDOP_UyX_7ti-_u7_fox2-wentbVJkNtILOyNKSFLLwsOq08kPQSvDVSCWipVNKjdroAAtkag1h2FqA12mjhOrDFil3_3Z5szRTDgHFpfo3NyVh8A53HS3k</recordid><startdate>20180813</startdate><enddate>20180813</enddate><creator>Aghaei, Sina</creator><creator>Langroudi, Amirreza Safari</creator><creator>Fekri, Masoud</creator><scope>ADEOX</scope><scope>GOX</scope></search><sort><creationdate>20180813</creationdate><title>A Predictive Model for Oil Market under Uncertainty: Data-Driven System Dynamics Approach</title><author>Aghaei, Sina ; Langroudi, Amirreza Safari ; Fekri, Masoud</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-8497e6143f43d65f0e4f40f874510be954fa6c630e04b77aa9d800b76761cd083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Quantitative Finance - Economics</topic><toplevel>online_resources</toplevel><creatorcontrib>Aghaei, Sina</creatorcontrib><creatorcontrib>Langroudi, Amirreza Safari</creatorcontrib><creatorcontrib>Fekri, Masoud</creatorcontrib><collection>arXiv Economics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Aghaei, Sina</au><au>Langroudi, Amirreza Safari</au><au>Fekri, Masoud</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Predictive Model for Oil Market under Uncertainty: Data-Driven System Dynamics Approach</atitle><date>2018-08-13</date><risdate>2018</risdate><abstract>In recent years, there have been a lot of sharp changes in the oil price.
These rapid changes cause the traditional models to fail in predicting the
price behavior. The main reason for the failure of the traditional models is
that they consider the actual value of parameters instead of their
expectational ones. In this paper, we propose a system dynamics model that
incorporates expectational variables in determining the oil price. In our
model, the oil price is determined by the expected demand and supply vs. their
actual values. Our core model is based upon regression analysis on several
historic time series and adjusted by adding many casual loops in the oil
market. The proposed model in simulated in different scenarios that have
happened in the past and our results comply with the trends of the oil price in
each of the scenarios.</abstract><doi>10.48550/arxiv.1808.04150</doi><oa>free_for_read</oa></addata></record> |
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subjects | Quantitative Finance - Economics |
title | A Predictive Model for Oil Market under Uncertainty: Data-Driven System Dynamics Approach |
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