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

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  • Clive Bowsher

    ()

  • Roland Meeks

    ()

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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Bibliographic Info

Paper provided by Oxford Financial Research Centre in its series OFRC Working Papers Series with number 2008fe24.

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Length: 40
Date of creation: 2008
Date of revision:
Handle: RePEc:sbs:wpsefe:2008fe24

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Web page: http://www.finance.ox.ac.uk
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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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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, Central Bank of Brazil, Research Department 247, Central Bank of Brazil, Research Department.
  2. repec:dgr:uvatin:0000041 is not listed on IDEAS
  3. 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.
  4. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2008. "An Arbitrage-Free Generalized Nelson-Siegel Term Structure Model," PIER Working Paper Archive 08-030, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  5. Koopman, Siem Jan & van der Wel, Michel, 2013. "Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(4), pages 676-694.
  6. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
  7. Caio Almeida & Axel Simonsen & José Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series, Central Bank of Brazil, Research Department 288, Central Bank of Brazil, Research Department.
  8. 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.
  9. 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.
  10. 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].
  11. Ito, Ryoko, 2013. "Modeling dynamic diurnal patterns in high frequency financial data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
  12. 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.
  13. 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.
  14. 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.

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