MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Nativa (Sinop) |
Texto Completo: | https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291 |
Resumo: | In the economic scenario, studying the behavior of prices of products, commodities or indicators makes it possible to make forecasts, allowing the elaboration of risk projections with greater precision and, when extreme events of these prices occur, losses or even bankruptcies can occur. In this sense, the Extreme Value Theory (EVT) is more suitable such phenomena. Economic data from CEPEA - ESALQ covering the period from 1997 to 2020 were used, organized in series of monthly maximums and, for each series, the Gumbel, Generalized Extreme Values (GVE) distributions and their non-stationary versions were considered. It could be seen that the Gumbel and GVE distributions fit in every month and the goodness-of-fit attest that the Gumbel distribution is the most suitable in every month. In the months of April to October there is a slight lower probability of prices being exceeded and in the months of November to February are the months with the highest probability of occurrence of high fat ox prices. The Mann-Kendall test was used for testing the trend in all series, which was incorporated into the non-stationary Gumbel distribution, and the likelihood ratio test and AIC were favorable in terms of trend modeling. |
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MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO Rural PlanningApplied EconomicsGumbel DistributionReturn ValueApplied StatisticsPlanejamento RuralEconomia AplicadaDistribuição GumbelValor de RetornoEstatística AplicadaIn the economic scenario, studying the behavior of prices of products, commodities or indicators makes it possible to make forecasts, allowing the elaboration of risk projections with greater precision and, when extreme events of these prices occur, losses or even bankruptcies can occur. In this sense, the Extreme Value Theory (EVT) is more suitable such phenomena. Economic data from CEPEA - ESALQ covering the period from 1997 to 2020 were used, organized in series of monthly maximums and, for each series, the Gumbel, Generalized Extreme Values (GVE) distributions and their non-stationary versions were considered. It could be seen that the Gumbel and GVE distributions fit in every month and the goodness-of-fit attest that the Gumbel distribution is the most suitable in every month. In the months of April to October there is a slight lower probability of prices being exceeded and in the months of November to February are the months with the highest probability of occurrence of high fat ox prices. The Mann-Kendall test was used for testing the trend in all series, which was incorporated into the non-stationary Gumbel distribution, and the likelihood ratio test and AIC were favorable in terms of trend modeling.No cenário econômico, estudar o comportamento de preços de produtos, commodities ou indicadores torna possível a realização de previsões, permitindo elaboração de projeções de risco com maior precisão e, quando ocorrem eventos extremos desses preços, perdas, ou até mesmo falências podem ocorrer. Nesse sentido, a Teoria de Valores Extremos (TVE) trata de maneira adequada tais fenômenos. Foram utilizados os dados econômicos do CEPEA - ESALQ compreendida entre o período de 1997 a 2020, organizados em séries de máximos mensais e, para cada série, as distribuições Gumbel, Generalizada de Valores Extremos (GVE) e suas versões não-estacionárias foram consideradas. Pôde-se constatar que as distribuições Gumbel e GVE se ajustaram em todos os meses e os indicadores de qualidade de ajuste atestam que a distribuição Gumbel é a mais adequada em todos os meses. Nos meses de abril a outubro existe uma ligeira menor probabilidade dos preços serem superados e nos meses de novembro a fevereiro são os meses com maior probabilidade de ocorrência de altos preços de boi gordo. Pelo teste de Mann-Kendall constatou-se tendência em todas as séries, a qual foi incorporada na distribuição Gumbel não-estacionária, e o teste de razão de verossimilhanças e AIC mostraram-se favoráveis quanto à modelagem da tendência. Palavras-chave: planejamento rural; economia aplicada; distribuição Gumbel; valor de retorno; estatística aplicada. Probabilistic modeling of maximum commodity fat ox prices for the state of São Paulo ABSTRACT: In the economic scenario, studying the behavior of prices of products, commodities or indicators makes it possible to make forecasts, allowing the elaboration of risk projections with greater precision and, when extreme events of these prices occur, losses or even bankruptcies can occur. In this sense, the Extreme Value Theory (EVT) is more suitable such phenomena. Economic data from CEPEA - ESALQ covering the period from 1997 to 2020 were used, organized in series of monthly maximums and, for each series, the Gumbel, Generalized Extreme Values (GVE) distributions and their non-stationary versions were considered. It could be seen that the Gumbel and GVE distributions fit in every month and the goodness-of-fit attest that the Gumbel distribution is the most suitable in every month. In the months of April to October there is a slight lower probability of prices being exceeded and in the months of November to February are the months with the highest probability of occurrence of high fat ox prices. The Mann-Kendall test was used for testing the trend in all series, which was incorporated into the non-stationary Gumbel distribution, and the likelihood ratio test and AIC were favorable in terms of trend modeling. Keywords: rural planning; applied economics; gumbel distribution; return value; applied statistics.Universidade Federal de Mato Grosso2023-09-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttps://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/1329110.31413/nativa.v10i1.13291Nativa; v. 10 n. 1 (2022); 22-31Nativa; Vol. 10 Núm. 1 (2022); 22-31Nativa; Vol. 10 No. 1 (2022); 22-312318-7670reponame:Nativa (Sinop)instname:Universidade Federal de Mato Grosso (UFMT)instacron:UFMTporhttps://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291/12650https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291/12651Copyright (c) 2022 Nativahttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessLucca Filho, Eduardo Lucca FilhoLiska, Gilberto RodriguesSantos, Jerônimo AlvesMatiussi, Ana Carolina2022-03-27T12:24:30Zoai:periodicoscientificos.ufmt.br:article/13291Revistahttps://periodicoscientificos.ufmt.br/ojs/index.php/nativaPUBhttps://periodicoscientificos.ufmt.br/ojs/index.php/nativa/oai||rrmelo2@yahoo.com.br2318-76702318-7670opendoar:2022-03-27T12:24:30Nativa (Sinop) - Universidade Federal de Mato Grosso (UFMT)false |
dc.title.none.fl_str_mv |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
title |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
spellingShingle |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO Lucca Filho, Eduardo Lucca Filho Rural Planning Applied Economics Gumbel Distribution Return Value Applied Statistics Planejamento Rural Economia Aplicada Distribuição Gumbel Valor de Retorno Estatística Aplicada |
title_short |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
title_full |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
title_fullStr |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
title_full_unstemmed |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
title_sort |
MODELAGEM PROBABILÍSTICA DE PREÇOS MÁXIMOS DA COMMODITY BOI GORDO PARA O ESTADO DE SÃO PAULO |
author |
Lucca Filho, Eduardo Lucca Filho |
author_facet |
Lucca Filho, Eduardo Lucca Filho Liska, Gilberto Rodrigues Santos, Jerônimo Alves Matiussi, Ana Carolina |
author_role |
author |
author2 |
Liska, Gilberto Rodrigues Santos, Jerônimo Alves Matiussi, Ana Carolina |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Lucca Filho, Eduardo Lucca Filho Liska, Gilberto Rodrigues Santos, Jerônimo Alves Matiussi, Ana Carolina |
dc.subject.por.fl_str_mv |
Rural Planning Applied Economics Gumbel Distribution Return Value Applied Statistics Planejamento Rural Economia Aplicada Distribuição Gumbel Valor de Retorno Estatística Aplicada |
topic |
Rural Planning Applied Economics Gumbel Distribution Return Value Applied Statistics Planejamento Rural Economia Aplicada Distribuição Gumbel Valor de Retorno Estatística Aplicada |
description |
In the economic scenario, studying the behavior of prices of products, commodities or indicators makes it possible to make forecasts, allowing the elaboration of risk projections with greater precision and, when extreme events of these prices occur, losses or even bankruptcies can occur. In this sense, the Extreme Value Theory (EVT) is more suitable such phenomena. Economic data from CEPEA - ESALQ covering the period from 1997 to 2020 were used, organized in series of monthly maximums and, for each series, the Gumbel, Generalized Extreme Values (GVE) distributions and their non-stationary versions were considered. It could be seen that the Gumbel and GVE distributions fit in every month and the goodness-of-fit attest that the Gumbel distribution is the most suitable in every month. In the months of April to October there is a slight lower probability of prices being exceeded and in the months of November to February are the months with the highest probability of occurrence of high fat ox prices. The Mann-Kendall test was used for testing the trend in all series, which was incorporated into the non-stationary Gumbel distribution, and the likelihood ratio test and AIC were favorable in terms of trend modeling. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-23 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291 10.31413/nativa.v10i1.13291 |
url |
https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291 |
identifier_str_mv |
10.31413/nativa.v10i1.13291 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291/12650 https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13291/12651 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Nativa https://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Nativa https://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de Mato Grosso |
publisher.none.fl_str_mv |
Universidade Federal de Mato Grosso |
dc.source.none.fl_str_mv |
Nativa; v. 10 n. 1 (2022); 22-31 Nativa; Vol. 10 Núm. 1 (2022); 22-31 Nativa; Vol. 10 No. 1 (2022); 22-31 2318-7670 reponame:Nativa (Sinop) instname:Universidade Federal de Mato Grosso (UFMT) instacron:UFMT |
instname_str |
Universidade Federal de Mato Grosso (UFMT) |
instacron_str |
UFMT |
institution |
UFMT |
reponame_str |
Nativa (Sinop) |
collection |
Nativa (Sinop) |
repository.name.fl_str_mv |
Nativa (Sinop) - Universidade Federal de Mato Grosso (UFMT) |
repository.mail.fl_str_mv |
||rrmelo2@yahoo.com.br |
_version_ |
1799711198076207104 |