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Movimentos da Estrutura a Termo e Critérios de Minimização do Erro de Previsão em um Modelo Paramétrico Exponencial

Listed author(s):
  • Caio Almeida
  • Romeu Gomes
  • André Leite
  • José Vicente

In this paper, we study how different choices of loadings affect forecasting in the exponential term structure model proposed by Diebold and Li (2006). The loadings are defined through a specific parameter lambda which controls both the decaying speed of the slope as well as the maximum of the curvature factors. In particular, adopting a database including Brazilian fixed income future contracts (ID future), we analyze four different rules of choices depending on metrics that minimize forecasting errors, for different forecasting horizons. We conclude that the optimal rule changes for different regions of ID future maturities/different forecasting horizons, indicating that the choice of how movements will be parameterized in this exponential model should be done with care, tailored for each particular application of the model.

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File URL: http://www.bcb.gov.br/pec/wps/port/wps146.pdf
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Paper provided by Central Bank of Brazil, Research Department in its series Working Papers Series with number 146.

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Date of creation: Oct 2007
Handle: RePEc:bcb:wpaper:146
Contact details of provider: Web page: http://www.bcb.gov.br/?english

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  1. Almeida, Caio & Vicente, José, 2008. "The role of no-arbitrage on forecasting: Lessons from a parametric term structure model," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2695-2705, December.
  2. Caio Almeida & Romeu Gomes & André Leite & Axel Simonsen & José Vicente, 2009. "Does Curvature Enhance Forecasting?," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(08), pages 1171-1196.
  3. 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.
  4. Svensson, L.E.O., 1993. "Monetary Policy with Flexible Exchange Rates and Foreward Interest Rates as Indicators," Papers 559, Stockholm - International Economic Studies.
  5. Gregory R. Duffee, 2002. "Term Premia and Interest Rate Forecasts in Affine Models," Journal of Finance, American Finance Association, vol. 57(1), pages 405-443, February.
  6. Vicente, José & Tabak, Benjamin M., 2008. "Forecasting bond yields in the Brazilian fixed income market," International Journal of Forecasting, Elsevier, vol. 24(3), pages 490-497.
  7. Eduardo J. A. Lima & Felipe Luduvice & Benjamin M. Tabak, 2006. "Forecasting Interest Rates: an application for Brazil," Working Papers Series 120, Central Bank of Brazil, Research Department.
  8. 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.
  9. Huse, Cristian, 2011. "Term structure modelling with observable state variables," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3240-3252.
  10. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
  11. Laurini, Márcio P. & Hotta, Luiz K., 2007. "Extensões Bayesianas do Modelo de Estrutura a Termo de Diebold-Li," Insper Working Papers wpe_88, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
  12. Varga, Gyorgy, 2009. "Teste de Modelos Estatísticos para a Estrutura a Termo no Brasil
    [Test of Term Structure Models for Brazil]
    ," MPRA Paper 20832, University Library of Munich, Germany.
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