[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/rim/rimwps/59_12.html
   My bibliography  Save this paper

Bayesian Semiparametric Dynamic Nelson-Siegel Model

Author

Listed:
  • Cem Çakmakli

    (Department of Quantitative Economics, University of Amsterdam, The Netherlands)

Abstract
This paper proposes the Bayesian semiparametric dynamic Nelson-Siegel model, where the density of the yield curve factors and thereby the density of the yields are estimated along with other model parameters. This is accomplished by modeling the error distributions of the factors according to a Dirichlet process mixture. An efficient and computationally tractable algorithm is implemented to obtain Bayesian inference. The semiparametric structure of the factors enables us to capture various forms of non-normalities including fat tails, skewness and nonlinear dependence between factors using a unified approach. The potential of the proposed framework is examined using US bond yields data. The results show that the model can identify two different periods with distinct characteristics. While the relatively stable years of late 1980s and 1990s comprise the first period, the second period captures the years of severe recessions including the recessions of 1970s and 1980s and the recent recession of 2007-9 together with highly volatile periods of Federal Reserve’s monetary policy experiments in the first half of 1980s. Interestingly, results point out a nonlinear dependence structure between the factors contrasting existing evidence.

Suggested Citation

  • Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper series 59_12, Rimini Centre for Economic Analysis, revised Sep 2012.
  • Handle: RePEc:rim:rimwps:59_12
    as

    Download full text from publisher

    File URL: http://www.rcea.org/RePEc/pdf/wp59_12.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
    2. Yu, Wei-Choun & Zivot, Eric, 2011. "Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 579-591.
    3. 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.
    4. 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.
    5. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.
    6. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    7. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
    8. Junker, Markus & Szimayer, Alex & Wagner, Niklas, 2006. "Nonlinear term structure dependence: Copula functions, empirics, and risk implications," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1171-1199, April.
    9. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    10. Kim, Don H. & Orphanides, Athanasios, 2012. "Term Structure Estimation with Survey Data on Interest Rate Forecasts," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(1), pages 241-272, February.
    11. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.
    12. Conley, Timothy G. & Hansen, Christian B. & McCulloch, Robert E. & Rossi, Peter E., 2008. "A semi-parametric Bayesian approach to the instrumental variable problem," Journal of Econometrics, Elsevier, vol. 144(1), pages 276-305, May.
    13. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    14. Friedman, Milton, 1984. "Lessons from the 1979-82 Monetary Policy Experiment," American Economic Review, American Economic Association, vol. 74(2), pages 397-400, May.
    15. Hautsch, Nikolaus & Ou, Yangguoyi, 2012. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2988-3007.
    16. Friedman, Benjamin M, 1984. "Lessons from the 1979-82 Monetary Policy Experiment," American Economic Review, American Economic Association, vol. 74(2), pages 382-387, May.
    17. Omori, Yasuhiro & Chib, Siddhartha & Shephard, Neil & Nakajima, Jouchi, 2007. "Stochastic volatility with leverage: Fast and efficient likelihood inference," Journal of Econometrics, Elsevier, vol. 140(2), pages 425-449, October.
    18. Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, vol. 110(1), pages 67-89, September.
    19. Bianchi, Francesco & Mumtaz, Haroon & Surico, Paolo, 2009. "Dynamics of the term structure of UK interest rates," Bank of England working papers 363, Bank of England.
    20. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    21. 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.
    22. McCulloch, J Huston, 1975. "The Tax-Adjusted Yield Curve," Journal of Finance, American Finance Association, vol. 30(3), pages 811-830, June.
    23. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    24. Mark J. Jensen, 2004. "Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 895-922, November.
    25. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 627-627, November.
    26. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    27. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
    28. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    29. 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.
    30. Ken Nyholm & Riccardo Rebonato, 2008. "Long-horizon yield curve projections: comparison of semi-parametric and parametric approaches," Applied Financial Economics, Taylor & Francis Journals, vol. 18(20), pages 1597-1611.
    31. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    32. Siegel, Andrew F. & Nelson, Charles R., 1988. "Long-Term Behavior of Yield Curves," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(1), pages 105-110, March.
    33. Dai, Qiang & Singleton, Kenneth J., 2002. "Expectation puzzles, time-varying risk premia, and affine models of the term structure," Journal of Financial Economics, Elsevier, vol. 63(3), pages 415-441, March.
    34. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Geert Mesters & Bernd Schwaab & Siem Jan Koopman, 2014. "A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area," Tinbergen Institute Discussion Papers 14-071/III, Tinbergen Institute.

    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. Guidolin, Massimo & Thornton, Daniel L., 2018. "Predictions of short-term rates and the expectations hypothesis," International Journal of Forecasting, Elsevier, vol. 34(4), pages 636-664.
    2. 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.
    3. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
    4. Siem Jan Koopman & Max I.P. Mallee & Michel van der Wel, 2007. "Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters," Tinbergen Institute Discussion Papers 07-095/4, Tinbergen Institute.
    5. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    6. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-71, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. 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.
    8. 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.
    9. 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.
    10. Makushkin, Mikhail & Lapshin, Victor, 2023. "Dynamic Nelson–Siegel model for market risk estimation of bonds: Practical implementation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 69, pages 5-27.
    11. 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.
    12. Hautsch, Nikolaus & Ou, Yangguoyi, 2012. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2988-3007.
    13. 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.
    14. Wali Ullah, 2017. "Term structure forecasting in affine framework with time-varying volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 453-483, August.
    15. 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.
    16. 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.
    17. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    18. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," Scandinavian Journal of Economics, Wiley Blackwell, vol. 106(2), pages 165-185, June.
    19. Wali Ullah & Yasumasa Matsuda & Yoshihiko Tsukuda, 2013. "Term Structure Modeling and Forecasting of Government Bond Yields," Economic Papers, The Economic Society of Australia, vol. 32(4), pages 535-560, December.
    20. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2018. "A term structure model under cyclical fluctuations in interest rates," Economic Modelling, Elsevier, vol. 72(C), pages 140-150.

    More about this item

    Keywords

    Dynamic factor model; Yield curve; Nelson-Siegel model; Dirichlet process mixture; Bayesian inference;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • 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:rim:rimwps:59_12. 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: Marco Savioli (email available below). General contact details of provider: https://edirc.repec.org/data/rcfeait.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.