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MCMC Based Estimation of Term Structure Models


  • Mikkelsen, Peter

    (Department of Finance, Aarhus School of Business)


We develop a state space framework for estimating term structure models, where latent Markovian state variables are mapped non-linearly into observable market data. The measurement equation of our framework is explicitly constructed such that it takes raw market prices and rates as direct inputs. We thus avoid entirely, the need for data preprocessing, such as the use of ad hoc interpolation and data smoothing techniques. As our general estimation approach, we demonstrate how Markov chain Monte Carlo techniques are well suited for handling complex functional relations between state vari-ables and data, parameter restrictions and other features of popular term structure mod-els, which have proved hard to handle for alternative econometric techniques. Our estimation framework therefore handles popular multi-factor model specifications such as exponential affine and quadratic models, but facilitates richer Markovian HJM model specifications as well. Efficient Markov chain Monte Carlo implementations are highly model dependent. Therefore, having developed the general estimation principles of our framework, we demonstrate how one could approach sampler specification for a particular model example which we fit to a panel data set of swap and money market rates.

Suggested Citation

  • Mikkelsen, Peter, 2001. "MCMC Based Estimation of Term Structure Models," Finance Working Papers 01-7, University of Aarhus, Aarhus School of Business, Department of Business Studies.
  • Handle: RePEc:hhb:aarfin:2001_007

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    References listed on IDEAS

    1. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
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    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    5. Tomas Björk & Bent Jesper Christensen, 1999. "Interest Rate Dynamics and Consistent Forward Rate Curves," Mathematical Finance, Wiley Blackwell, vol. 9(4), pages 323-348.
    6. Duffie, Darrell & Singleton, Kenneth J, 1997. " An Econometric Model of the Term Structure of Interest-Rate Swap Yields," Journal of Finance, American Finance Association, vol. 52(4), pages 1287-1321, September.
    7. Chan, K C, et al, 1992. " An Empirical Comparison of Alternative Models of the Short-Term Interest Rate," Journal of Finance, American Finance Association, vol. 47(3), pages 1209-1227, July.
    8. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    9. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
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    Cited by:

    1. Peter Feldhütter, 2016. "Can Affine Models Match the Moments in Bond Yields?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-56, June.

    More about this item


    Non-linear State Space; MCMC; HJM; Factor Models;


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