Forecasting Latin-American yield curves: An artificial neural network approach

Daniel Vela ()

Borradores de Economia from Banco de la Republica de Colombia

Abstract: This document explores the predictive power of the yield curves in Latin America (Colombia, Mexico, Peru and Chile) taking into account the factors set by the specifications of Nelson & Siegel and Svensson. Several forecasting methodologies are contrasted: an autoregressive model, a vector autoregressive model, artificial neural networks on each individual factor, and artificial neural networks on all factors that explain the yield curve. The out-of-sample performance of the fitting models improves with the neural networks in the one-month-ahead forecast along all studied yield curves. Moreover, the three factor model developed by Nelson & Siegel proves to be the best choice for out-of-sample forecasting. Finally, the success of the cross variable interaction strongly depends on the selected yield curve.

Keywords: Term structure of interest rates; Nelson & Siegel; Svensson; out-of-sample forecast; Artificial Neural Networks. (search for similar items in EconPapers)
JEL-codes: C32 C45 E43 G17 (search for similar items in EconPapers)
Pages: 28
Date: 2013-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:bdr:borrec:761

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