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

Author

Listed:
  • Andreasen, Martin

    () (Aarhus University)

  • Meldrum, Andrew

    () (Bank of England)

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.

Suggested Citation

  • Andreasen, Martin & Meldrum, Andrew, 2013. "Likelihood inference in non-linear term structure models: the importance of the lower bound," Bank of England working papers 481, Bank of England.
  • Handle: RePEc:boe:boeewp:0481
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    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2013/likelihood-inference-in-non-linear-term-structure-models-the-importance-of-the-lower-bound.pdf
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    References listed on IDEAS

    as
    1. Carrasco, Marine & Florens, Jean-Pierre, 2002. "Simulation-Based Method of Moments and Efficiency," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 482-492, October.
    2. 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.
    3. 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.
    4. 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.
    5. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(05), pages 933-956, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Malik, Sheheryar & Meldrum, Andrew, 2016. "Evaluating the robustness of UK term structure decompositions using linear regression methods," Journal of Banking & Finance, Elsevier, vol. 67(C), pages 85-102.
    2. Marcello Pericoli & Marco Taboga, 2015. "Understanding policy rates at the zero lower bound: insights from a Bayesian shadow rate model," Temi di discussione (Economic working papers) 1023, Bank of Italy, Economic Research and International Relations Area.
    3. Renne Jean-Paul, 2017. "A model of the euro-area yield curve with discrete policy rates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 99-116, February.

    More about this item

    Keywords

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

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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