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Master's Dissertation
DOI
10.11606/D.96.2017.tde-27102017-102841
Document
Author
Full name
Hugo Mamoru Aoki Hissanaga
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Ribeirão Preto, 2017
Supervisor
Committee
Laurini, Marcio Poletti (President)
Caldeira, João Frois
Gomes, Fabio Augusto Reis
Palma, Andreza Aparecida
Title in Portuguese
Previsão da curva de juros com análise de componentes principais utilizando matriz de covariâcia de longo prazo
Keywords in Portuguese
Análise de componentes principais
Curva de juros
Previsão
Robustez
Abstract in Portuguese
Apesar da Análise de Componentes Principais (PCA) ser um dos métodos mais importantes na análise da estrutura a termo de taxa de juros, há fortes indícios de não ser adequada para estimar fatores da curva de juros quando há presença de dependência temporal e erros de medida. Para corrigir esses problemas é indicado utilizar a matriz de covariância de longo prazo, extraindo a correta estrutura de covariância presente nestes processos. Neste trabalho, mostramos que realizar a previsão fora da amostra da curva de taxa de juros com o método de Análise de Componentes Principais (PCA) utilizando como base a matriz de covarância de longo prazo (LRCM) parece ser mais acurada comparada a PCA com base na matriz de covariância amostral.
Title in English
Forecast of the interest curve with principal components analysis using long-term covariance matrix
Keywords in English
Forecasting
Interest curve
Principal components analysis
Robustness
Abstract in English
Although Principal Component Analysis (PCA) is one of the most common methods to estimate the structure of interest rate volatility, there are strong indications that it is not adequate to estimate interest rate factors when there is temporal dependence and measurement errors. To correct these problems it is necessary to use the longterm covariance matrix, to extract the correct covariance structure present in these processes. In this work, we show that performing the out-of-sample forecasting of the interest rate curve with the Principal Component Analysis (PCA) method based on the long-term covariance matrix (LRCM) seems to be more accurate compared to PCA based on sample covariance matrix.
 
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Publishing Date
2017-11-27
 
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