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

  • Daniel Vela

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    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.

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    File URL: http://www.banrep.gov.co/docum/ftp/be_761.pdf
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    Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 010502.

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    Length: 28
    Date of creation: 28 Feb 2013
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    Handle: RePEc:col:000094:010502
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    4. Rafael B. Rezende & Mauro S. Ferreira, 2013. "Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(2), pages 111-123, 03.
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    7. de Menezes, Lilian M. & Nikolaev, Nikolay Y., 2006. "Forecasting with genetically programmed polynomial neural networks," International Journal of Forecasting, Elsevier, vol. 22(2), pages 249-265.
    8. Diebold, Francis X. & Li, Canlin & Yue, Vivian Z., 2008. "Global yield curve dynamics and interactions: A dynamic Nelson-Siegel approach," Journal of Econometrics, Elsevier, vol. 146(2), pages 351-363, October.
    9. David Jamieson Bolder & Scott Gusba, 2002. "Exponentials, Polynomials, and Fourier Series: More Yield Curve Modelling at the Bank of Canada," Working Papers 02-29, Bank of Canada.
    10. Bodyanskiy, Yevgeniy & Popov, Sergiy, 2006. "Neural network approach to forecasting of quasiperiodic financial time series," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1357-1366, December.
    11. David Jamieson Bolder, 2006. "Modelling Term-Structure Dynamics for Risk Management: A Practitioner's Perspective," Working Papers 06-48, Bank of Canada.
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