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The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve

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Author Info

  • Clive G. Bowsher

    () (Nuffield College, Oxford University)

  • Roland Meeks

    () (Federal Reserve Bank of Dallas)

Abstract

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or `signal') is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or 'y-values') at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear, state space model. This FSN model achieves dimension reduction, provides a coherent description of the observed yield curve and its dynamics as the cross-sectional dimension N becomes large, and can feasibly be estimated and used for forecasting when N is large. The integration and cointegration properties of the model are derived. The FSN models are then applied to forecasting 36-dimensional yield curves for US Treasury bonds at the one month ahead horizon. The method consistently outperforms the Diebold and Li (2006) and random walk forecasts on the basis of both mean square forecast error criteria and economically relevant loss functions derived from the realised profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempts to infer the relative economic value of model forecasts on the basis of their associated mean square forecast errors.

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File URL: http://www.nuffield.ox.ac.uk/economics/papers/2008/w5/FSN.ECM_YCurve_Bowsher.pdf
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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2008-W05.

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Length: 40 pages
Date of creation: 04 2008
Date of revision:
Handle: RePEc:nuf:econwp:0805

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Web page: http://www.nuff.ox.ac.uk/economics/

Related research

Keywords: FSN-ECM models; functional time series; term structure; forecasting interest rates; natural cubic spline; state space form.;

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References

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  1. Francis X. Diebold & Canlin Li, 2004. "Forecasting the Term Structure of Government Bond Yields," CFS Working Paper Series 2004/09, Center for Financial Studies.
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Citations

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Cited by:
  1. Benjamin M. Tabak & Daniel O. Cajueiro & Alexandre B. Sollaci, 2011. "Forecasting the Yield Curve for the Euro Region," Working Papers Series 247, Central Bank of Brazil, Research Department.
  2. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2008. "An arbitrage-free generalized Nelson-Siegel term structure model," Working Paper Series 2008-07, Federal Reserve Bank of San Francisco.
  3. Jose Vicente & Benjamin M. Tabak, 2007. "Forecasting Bonds Yields in the Brazilian Fixed Income Market," Working Papers Series 141, Central Bank of Brazil, Research Department.
  4. Wolfgang Karl Härdle,Piotr Majer & Melanie Schienle, 2012. "Yield Curve Modeling and Forecasting using Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2012-048, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Siem Jan Koopman & Michel van der Wel, 2011. "Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model," Tinbergen Institute Discussion Papers 11-063/4, Tinbergen Institute.
  6. Joao Frois Caldeira & Guilherme Valle Moura & Marcelo Savino Portugal, 2011. "Efficient Interest Ratecurve Estimation And Forecasting In Brazil," Anais do XXXVII Encontro Nacional de Economia [Proceedings of the 37th Brazilian Economics Meeting] 133, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
  7. Ito, Ryoko, 2013. "Modeling dynamic diurnal patterns in high frequency financial data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
  8. Borus Jungbacker & Siem Jan Koopman & Michel van der Wel, 2009. "Smooth Dynamic Factor Analysis with an Application to the U.S. Term Structure of Interest Rates," CREATES Research Papers 2009-39, School of Economics and Management, University of Aarhus.
  9. Dick van Dijk & Siem Jan Koopman & Michel van der Wel & Jonathan H. Wright, 2012. "Forecasting Interest Rates with Shifting Endpoints," Tinbergen Institute Discussion Papers 12-076/4, Tinbergen Institute.
  10. Borus Jungbacker & Siem Jan Koopman & Michel van der Wel, 0000. "Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates," Tinbergen Institute Discussion Papers 09-041/4, Tinbergen Institute, revised 17 Sep 2010.
  11. repec:dgr:uvatin:20090041 is not listed on IDEAS
  12. Caio Almeida & Axel Simonsen & José Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
  13. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.

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