Forecasting the U.S. Term Structure of Interest Rates using Nonparametric Functional Data Analysis

21 Pages Posted: 7 Jun 2012 Last revised: 3 Jul 2016

See all articles by João Caldeira

João Caldeira

Universidade Federal de Santa Catarina & CNPq

Hudson Torrent

Universidade Federal do Rio Grande do Sul (UFRGS) - Department of Statistics

Date Written: April 7, 2013

Abstract

In this paper we consider a novel procedure for forecasting the US yield curve by using the methodology of nonparametric kernel estimation of functional data (NP-FDA). Within this approach, each element of the sample is a monthly yield curve, evaluated at points corresponding to maturities. In this framework we attempt to capture the dynamics present in the sample of curves to forecast future values for the yield at a given maturity without imposing any parametric structure. In order to evaluated forecast performance of the proposed estimator, we consider four forecast horizons and the results are compared with widely known parametric models. Our estimates with NP-FDA present predictive performance superior to its competitors in many situations considered, especially at longer time horizons for long-term maturities. The methodol- ogy applied in this paper may be important for policy makers, fixed income portfolio managers, financial institutions and academics as it may prove useful in the construction of long-term scenarios for the yield curve.

Keywords: Term structure estimation, factor models, nonparametric method, Interest rate forecasting, Kalman filter

JEL Classification: C53, E43, G17

Suggested Citation

Caldeira, João and Torrent, Hudson, Forecasting the U.S. Term Structure of Interest Rates using Nonparametric Functional Data Analysis (April 7, 2013). Available at SSRN: https://ssrn.com/abstract=2079795 or http://dx.doi.org/10.2139/ssrn.2079795

João Caldeira (Contact Author)

Universidade Federal de Santa Catarina & CNPq ( email )

R. Eng. Agronômico Andrei Cristian Ferreira, s/n
Florianópolis, SC Rio Grande do Sul 90480-004
Brazil

Hudson Torrent

Universidade Federal do Rio Grande do Sul (UFRGS) - Department of Statistics ( email )

Instituto de Matemática - UFRGS
Av. Bento Gonçalves, 9500 - Prédio 43-111 - Agrono
Porto Alegre, RS 91509-900
Brazil

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