Previsão da estrutura a termo de cupom cambial

Detalhes bibliográficos
Autor(a) principal: Barbosa, Diego Makasevicius
Data de Publicação: 2017
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: http://hdl.handle.net/10438/19300
Resumo: This paper proposes to apply a similar framework adopted by Diebold and Li (2006) to forecast the Brazilian term structure of the US dollar-denominated interest rates, which have been done through the well-known three factors model developed by Nelson-Siegel. The methodology used to find the lambda factor, which drives the decay velocity of interest rates, was the rolling window optimization where for each forecast was calculated the lambda that minimizes the root mean square error (RMSE) of Nelson and Siegel fit. Furthermore, an autoregressive model was used to estimate the latent factors and, consequently, the interest rate. The results obtained were analogous to those found by Diebold and Li, where the authors verified a good predictive capacity for the model when compared to the random walk and other models used as benchmark.
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spelling Barbosa, Diego MakaseviciusEscolas::EPGEFGVVicente, JoséGonçalves, Edson Daniel LopesGlasman, Daniela Kubudi2017-12-08T16:46:06Z2017-12-08T16:46:06Z2017-09-25http://hdl.handle.net/10438/19300This paper proposes to apply a similar framework adopted by Diebold and Li (2006) to forecast the Brazilian term structure of the US dollar-denominated interest rates, which have been done through the well-known three factors model developed by Nelson-Siegel. The methodology used to find the lambda factor, which drives the decay velocity of interest rates, was the rolling window optimization where for each forecast was calculated the lambda that minimizes the root mean square error (RMSE) of Nelson and Siegel fit. Furthermore, an autoregressive model was used to estimate the latent factors and, consequently, the interest rate. The results obtained were analogous to those found by Diebold and Li, where the authors verified a good predictive capacity for the model when compared to the random walk and other models used as benchmark.O presente trabalho concentra-se em fazer um exercício de previsão da curva de cupom cambial futura similar ao proposto por Diebold e Li (2006) para as treasuries americanas, onde os autores utilizam um modelo econométrico de três fatores, no caso o conhecido Nelson e Siegel. A metodologia adotada para encontrar o fator λ (lambda), parâmetro este que rege a velocidade de decaimento da taxa do cupom cambial, foi uma otimização utilizando uma janela móvel, onde para cada instante t é observado qual o lambda que minimizaria a raiz do erro quadrático médio (REQM) do fit do modelo de Nelson-Siegel. Em seguida é conduzido um modelo autoregressivo para estimar os fatores latentes e consequentemente a taxa de cupom cambial para o exercício. O resultado obtido foi em linha com o encontrado por Diebold e Li onde os autores constataram uma boa capacidade preditiva para o modelo quando comparado ao passeio aleatório, nosso benchmark.porDollar-denominated interest ratesForcastingTerm structureCupom cambialPrevisãoEstrutura a termoFinançasTaxa de jurosInvestimentos - AnáliseMercado financeiroPrevisão da estrutura a termo de cupom cambialinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessTEXTTrabalho Final (Assinada).pdf.txtTrabalho Final (Assinada).pdf.txtExtracted texttext/plain55226https://repositorio.fgv.br/bitstreams/95c6d039-adc1-46bd-9507-ac9d5cca0fa5/downloadd32d8c59d0e56455215cc6fa77bca68eMD57ORIGINALTrabalho Final (Assinada).pdfTrabalho Final 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dc.title.por.fl_str_mv Previsão da estrutura a termo de cupom cambial
title Previsão da estrutura a termo de cupom cambial
spellingShingle Previsão da estrutura a termo de cupom cambial
Barbosa, Diego Makasevicius
Dollar-denominated interest rates
Forcasting
Term structure
Cupom cambial
Previsão
Estrutura a termo
Finanças
Taxa de juros
Investimentos - Análise
Mercado financeiro
title_short Previsão da estrutura a termo de cupom cambial
title_full Previsão da estrutura a termo de cupom cambial
title_fullStr Previsão da estrutura a termo de cupom cambial
title_full_unstemmed Previsão da estrutura a termo de cupom cambial
title_sort Previsão da estrutura a termo de cupom cambial
author Barbosa, Diego Makasevicius
author_facet Barbosa, Diego Makasevicius
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EPGE
dc.contributor.affiliation.none.fl_str_mv FGV
dc.contributor.member.none.fl_str_mv Vicente, José
Gonçalves, Edson Daniel Lopes
dc.contributor.author.fl_str_mv Barbosa, Diego Makasevicius
dc.contributor.advisor1.fl_str_mv Glasman, Daniela Kubudi
contributor_str_mv Glasman, Daniela Kubudi
dc.subject.eng.fl_str_mv Dollar-denominated interest rates
Forcasting
Term structure
topic Dollar-denominated interest rates
Forcasting
Term structure
Cupom cambial
Previsão
Estrutura a termo
Finanças
Taxa de juros
Investimentos - Análise
Mercado financeiro
dc.subject.por.fl_str_mv Cupom cambial
Previsão
Estrutura a termo
dc.subject.area.por.fl_str_mv Finanças
dc.subject.bibliodata.por.fl_str_mv Taxa de juros
Investimentos - Análise
Mercado financeiro
description This paper proposes to apply a similar framework adopted by Diebold and Li (2006) to forecast the Brazilian term structure of the US dollar-denominated interest rates, which have been done through the well-known three factors model developed by Nelson-Siegel. The methodology used to find the lambda factor, which drives the decay velocity of interest rates, was the rolling window optimization where for each forecast was calculated the lambda that minimizes the root mean square error (RMSE) of Nelson and Siegel fit. Furthermore, an autoregressive model was used to estimate the latent factors and, consequently, the interest rate. The results obtained were analogous to those found by Diebold and Li, where the authors verified a good predictive capacity for the model when compared to the random walk and other models used as benchmark.
publishDate 2017
dc.date.accessioned.fl_str_mv 2017-12-08T16:46:06Z
dc.date.available.fl_str_mv 2017-12-08T16:46:06Z
dc.date.issued.fl_str_mv 2017-09-25
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