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Likelihood inference in non-linear term structure models: the importance of the lower bound

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  • Andreasen, Martin

    ()
    (Aarhus University)

  • Meldrum, Andrew

    ()
    (Bank of England)

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    Abstract

    This paper shows how to use adaptive particle filtering and Markov chain Monte Carlo methods to estimate quadratic term structure models (QTSMs) by likelihood inference. The procedure is applied to a quadratic model for the United States during the recent financial crisis. We find that this model provides a better statistical description of the data than a Gaussian affine term structure model. In addition, QTSMs account perfectly for the lower bound whereas Gaussian affine models frequently imply forecast distributions with negative interest rates. Such predictions appear during the recent financial crisis but also prior to the crisis.

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    File URL: http://www.bankofengland.co.uk/research/Documents/workingpapers/2013/wp481.pdf
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    Bibliographic Info

    Paper provided by Bank of England in its series Bank of England working papers with number 481.

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    Length: 35 pages
    Date of creation: 20 Dec 2013
    Date of revision:
    Handle: RePEc:boe:boeewp:0481

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    Related research

    Keywords: Adaptive particle filtering; Bayesian inference; Higher-order moments; PMCMC; Quadratic term structure models;

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    1. Markus Leippold & Liuren Wu, 2002. "Asset Pricing Under The Quadratic Class," Finance, EconWPA 0207015, EconWPA.
    2. Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series, Oxford Financial Research Centre 2008fe32, Oxford Financial Research Centre.
    3. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342.
    4. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Papers, University of Pittsburgh, Department of Economics 321, University of Pittsburgh, Department of Economics, revised Jan 2007.
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    7. Don H Kim, 2007. "Spanned stochastic volatility in bond markets: a reexamination of the relative pricing between bonds and bond options," BIS Working Papers 239, Bank for International Settlements.
    8. Martin M. Andreasen, 2010. "Non-linear DSGE Models and The Optimized Particle Filter," CREATES Research Papers 2010-05, School of Economics and Management, University of Aarhus.
    9. Chib, Siddhartha & Ergashev, Bakhodir, 2009. "Analysis of Multifactor Affine Yield Curve Models," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 104(488), pages 1324-1337.
    10. Marco Realdon, 2006. "Quadratic Term Structure Models in Discrete Time," Discussion Papers, Department of Economics, University of York 06/01, Department of Economics, University of York.
    11. Dong-Hyun Ahn & Robert F. Dittmar, 2002. "Quadratic Term Structure Models: Theory and Evidence," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 15(1), pages 243-288, March.
    12. Neil Shephard & Thomas Flury, 2009. "Learning and filtering via simulation: smoothly jittered particle filters," Economics Series Working Papers 469, University of Oxford, Department of Economics.
    13. Carrasco, Marine & Florens, Jean-Pierre, 2002. "Simulation-Based Method of Moments and Efficiency," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(4), pages 482-92, October.
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