IDEAS home Printed from https://ideas.repec.org/p/ibr/dpaper/2011-02.html
   My bibliography  Save this paper

Bayesian Factor Selection in Dynamic Term Structure Models

Author

Listed:
  • Márcio Laurini

    (IBMEC Business School)

Abstract

This paper discusses Bayesian procedures for factor selection in dynamic term structure models through simulation methods based on Markov Chain Monte Carlo. The number of factors, besides influencing the fitting and prediction of observed yields, is also relevant to features such as the imposition of no-arbitrage conditions. We present a methodology for selecting the best specification in the Nelson-Siegel class of models using Reversible Jump MCMC.

Suggested Citation

  • Márcio Laurini, 2011. "Bayesian Factor Selection in Dynamic Term Structure Models," IBMEC RJ Economics Discussion Papers 2011-02, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    • Handle: RePEc:ibr:dpaper:2011-02
    as

    Download full text from publisher

    File URL: http://professores.ibmecrj.br/erg/dp/papers/dp201102.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christensen, Jens H.E. & Diebold, Francis X. & Rudebusch, Glenn D., 2011. "The affine arbitrage-free class of Nelson-Siegel term structure models," Journal of Econometrics, Elsevier, vol. 164(1), pages 4-20, September.
    2. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2009. "An arbitrage-free generalized Nelson--Siegel term structure model," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 33-64, November.
    3. 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.
    4. Scott Joslin & Kenneth J. Singleton & Haoxiang Zhu, 2011. "A New Perspective on Gaussian Dynamic Term Structure Models," Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 926-970.
    5. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
    6. Tomas Björk & Bent Jesper Christensen, 1999. "Interest Rate Dynamics and Consistent Forward Rate Curves," Mathematical Finance, Wiley Blackwell, vol. 9(4), pages 323-348, October.
    7. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    8. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, January.
    9. Fama, Eugene F & Bliss, Robert R, 1987. "The Information in Long-Maturity Forward Rates," American Economic Review, American Economic Association, vol. 77(4), pages 680-692, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    2. repec:jss:jstsof:36:i01 is not listed on IDEAS
    3. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    4. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 16(1), pages 1-33.
    5. Leo Krippner, 2009. "A theoretical foundation for the Nelson and Siegel class of yield curve models," Reserve Bank of New Zealand Discussion Paper Series DP2009/10, Reserve Bank of New Zealand.
    6. Wali Ullah, 2020. "The arbitrage-free generalized Nelson–Siegel term structure model: Does a good in-sample fit imply better out-of-sample forecasts?," Empirical Economics, Springer, vol. 59(3), pages 1243-1284, September.
    7. Michal Dvorák & Zlatuše Komárková & Adam Kucera, 2019. "The Czech Government Yield Curve Decomposition at the Lower Bound," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(1), pages 2-36, February.
    8. Konstantinos Bisiotis & Stelios Psarakis & Athanasios N. Yannacopoulos, 2022. "Affine Term Structure Models: Applications in Portfolio Optimization and Change Point Detection," Mathematics, MDPI, vol. 10(21), pages 1-33, November.
    9. Wali Ullah & Yasumasa Matsuda, 2014. "Generalized Nelson-Siegel Term Structure Model : Do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast?," TERG Discussion Papers 312, Graduate School of Economics and Management, Tohoku University.
    10. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Takamizawa, Hideyuki, 2022. "How arbitrage-free is the Nelson–Siegel model under stochastic volatility?," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 205-223.
    12. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," SIRE Discussion Papers 2015-71, Scottish Institute for Research in Economics (SIRE).
    13. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2010. "Inflation Expectations and Risk Premiums in an Arbitrage‐Free Model of Nominal and Real Bond Yields," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 143-178, September.
    14. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    15. Bruno Feunou & Jean-Sébastien Fontaine & Anh Le & Christian Lundblad, 2022. "Tractable Term Structure Models," Management Science, INFORMS, vol. 68(11), pages 8411-8429, November.
    16. Erhard RESCHENHOFER & Thomas STARK, 2019. "Forecasting the Yield Curve with Dynamic Factors," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 101-113, March.
    17. Duffee, Gregory, 2013. "Forecasting Interest Rates," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 385-426, Elsevier.
    18. Rafael Barros de Rezende, 2011. "Giving Flexibility to the Nelson-Siegel Class of Term Structure Models," Brazilian Review of Finance, Brazilian Society of Finance, vol. 9(1), pages 27-49.
    19. Caldeira, João F. & Laurini, Márcio P. & Portugal, Marcelo S., 2010. "Bayesian Inference Applied to Dynamic Nelson-Siegel Model with Stochastic Volatility," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
    20. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    21. Levant, Jared & Ma, Jun, 2017. "A dynamic Nelson-Siegel yield curve model with Markov switching," Economic Modelling, Elsevier, vol. 67(C), pages 73-87.

    More about this item

    Keywords

    Dynamic Term Structure Models; Model Selection; Reversible Jump MCMC;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibr:dpaper:2011-02. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Márcio Laurini (email available below). General contact details of provider: https://edirc.repec.org/data/ibmrjbr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.