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HISSANAGA, H. M. A. Forecast of the interest curve with principal components analysis using long-term covariance matrix. 2017. Dissertation (Master Degree) Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, 2017. 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. Key-words: Forecasting, Principal Components Analysis, Robustness, Interest Curve

Previsão da curva de juros com análise de componentes principais utilizando matriz de covariâcia de longo prazo