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

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  • Daniel Vela

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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.

Suggested Citation

  • Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," BORRADORES DE ECONOMIA 010502, BANCO DE LA REPÚBLICA.
  • Handle: RePEc:col:000094:010502
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    File URL: http://www.banrep.gov.co/docum/ftp/be_761.pdf
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    References listed on IDEAS

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    1. Tomas Björk & Bent Jesper Christensen, 1999. "Interest Rate Dynamics and Consistent Forward Rate Curves," Mathematical Finance, Wiley Blackwell, vol. 9(4), pages 323-348.
    2. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    3. 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.
    4. 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.
    5. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2009. "An arbitrage-free generalized Nelson--Siegel term structure model," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 33-64, November.
    6. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. 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, March.
    9. 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.
    10. David Jamieson Bolder, 2006. "Modelling Term-Structure Dynamics for Risk Management: A Practitioner's Perspective," Staff Working Papers 06-48, Bank of Canada.
    11. David Jamieson Bolder & Scott Gusba, 2002. "Exponentials, Polynomials, and Fourier Series: More Yield Curve Modelling at the Bank of Canada," Staff Working Papers 02-29, Bank of Canada.
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    Cited by:

    1. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.

    More about this item

    Keywords

    Term structure of interest rates; Nelson & Siegel; Svensson; out-of-sample forecast; Artificial Neural Networks.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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