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Modelling Term-Structure Dynamics for Risk Management: A Practitioner's Perspective

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  • David Bolder

Abstract

Modelling term-structure dynamics is an important component in measuring and managing the exposure of portfolios to adverse movements in interest rates. Model selection from the enormous term-structure literature is far from obvious and, to make matters worse, a number of recent papers have called into question the ability of some of the more popular models to adequately describe interest rate dynamics. The author, in attempting to find a relatively simple term-structure model that does a reasonable job of describing interest rate dynamics for risk-management purposes, examines two sets of models. The first set involves variations of the Gaussian affine term-structure model by modestly building on the recent work of Dai and Singleton (2000) and Duffee (2002). The second set includes and extends Diebold and Li (2003). After working through the mathematical derivation and estimation of these models, the author compares and contrasts their performance on a number of in- and out-of-sample forecasting metrics, their ability to capture deviations from the expectations hypothesis, and their predictions in a simple portfolio-optimization setting. He finds that the extended Nelson-Siegel model and an associated generalization, what he terms the "exponential-spline model," provide the most appealing modelling alternatives when considering the various model criteria.

Suggested Citation

  • David Bolder, 2006. "Modelling Term-Structure Dynamics for Risk Management: A Practitioner's Perspective," Staff Working Papers 06-48, Bank of Canada.
  • Handle: RePEc:bca:bocawp:06-48
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    References listed on IDEAS

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    1. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," American Economic Review, American Economic Association, vol. 95(2), pages 415-420, May.
    2. Backus, David & Foresi, Silverio & Mozumdar, Abon & Wu, Liuren, 2001. "Predictable changes in yields and forward rates," Journal of Financial Economics, Elsevier, vol. 59(3), pages 281-311, March.
    3. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    4. David Bolder & Grahame Johnson & Adam Metzler, 2004. "An Empirical Analysis of the Canadian Term Structure of Zero-Coupon Interest Rates," Staff Working Papers 04-48, Bank of Canada.
    5. 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.
    6. 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.
    7. Dong-Hyun Ahn & Robert F. Dittmar, 2002. "Quadratic Term Structure Models: Theory and Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 15(1), pages 243-288, March.
    8. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, October.
    9. John H. Cochrane & Monika Piazzesi, 2005. "Bond Risk Premia," American Economic Review, American Economic Association, vol. 95(1), pages 138-160, March.
    10. Monika Piazzesi, 2005. "Bond Yields and the Federal Reserve," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 311-344, April.
    11. David 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.
    12. David Bolder & David Stréliski, 1999. "Yield Curve Modelling at the Bank of Canada," Technical Reports 84, Bank of Canada.
    13. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
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    Cited by:

    1. David Bolder & Tiago Rubin, 2007. "Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis," Staff Working Papers 07-13, Bank of Canada.
    2. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 10502, Banco de la Republica.
    3. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 761, Banco de la Republica de Colombia.
    4. Michele Manna & Emmanuela Bernardini & Mauro Bufano & Davide Dottori, 2013. "Modelling public debt strategies," Questioni di Economia e Finanza (Occasional Papers) 199, Bank of Italy, Economic Research and International Relations Area.
    5. Lapshin, Victor & Sohatskaya, Sofia, 2020. "Choosing the weighting coefficients for estimating the term structure from sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 635-648.
    6. Francisco Rivadeneyra & Oumar Dissou, 2011. "A Model of the EFA Liabilities," Discussion Papers 11-11, Bank of Canada.
    7. Felix Geiger, 2009. "International Interest-Rate Risk Premia in Affine Term Structure Models," Diskussionspapiere aus dem Institut für Volkswirtschaftslehre der Universität Hohenheim 316/2009, Department of Economics, University of Hohenheim, Germany.
    8. David Jamieson Bolder & Yuliya Romanyuk, 2010. "Combining Canadian Interest Rate Forecasts," Palgrave Macmillan Books, in: Arjan B. Berkelaar & Joachim Coche & Ken Nyholm (ed.), Interest Rate Models, Asset Allocation and Quantitative Techniques for Central Banks and Sovereign Wealth Funds, chapter 1, pages 3-30, Palgrave Macmillan.
    9. Renne, J-P., 2009. "Frequency-domain analysis of debt service in a macro-finance model for the euro area," Working papers 261, Banque de France.

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    More about this item

    Keywords

    Interest rates; Econometric and statistical methods; Financial markets;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • G1 - Financial Economics - - General Financial Markets

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