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Bayesian inference in a Stochastic Volatility Nelson–Siegel model

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  • Hautsch, Nikolaus
  • Yang, Fuyu

Abstract

Bayesian inference is developed and applied for an extended Nelson–Siegel term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson–Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. A Markov chain Monte Carlo (MCMC) algorithm is proposed to efficiently estimate the SVNS model using simulation-based inference. The SVNS model is applied to monthly US zero-coupon yields. Significant evidence for time-varying volatility in the yield factors is found. The inclusion of stochastic volatility improves the model’s goodness-of-fit and clearly reduces the forecasting uncertainty, particularly in low-volatility periods. The proposed approach is shown to work efficiently and is easily adapted to alternative specifications of dynamic factor models revealing (multivariate) stochastic volatility.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3774-3792
    DOI: 10.1016/j.csda.2010.07.003
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    8. 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).
    9. Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.
    10. Enno Mammen & Christoph Rothe & Melanie Schienle, 2010. "Nonparametric Regression with Nonparametrically Generated Covariates," SFB 649 Discussion Papers SFB649DP2010-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. 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.
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    13. Márcio Poletti Laurini & Armênio Westin Neto, 2014. "Arbitrage in the Term Structure of Interest Rates: a Bayesian Approach," International Econometric Review (IER), Econometric Research Association, vol. 6(2), pages 77-99, September.
    14. Ralf Sabiwalsky, 2010. "Executive Compensation Regulation and the Dynamics of the Pay-Performance Sensitivity," SFB 649 Discussion Papers SFB649DP2010-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Raviv, Eran, 2015. "Prediction bias correction for dynamic term structure models," Economics Letters, Elsevier, vol. 129(C), pages 112-115.
    16. Wolfgang Karl Härdle & Rouslan Moro & Linda Hoffmann, 2010. "Learning Machines Supporting Bankruptcy Prediction," SFB 649 Discussion Papers SFB649DP2010-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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    21. Alexander L. Baranovski, 2010. "Dynamical systems forced by shot noise as a new paradigm in the interest rate modeling," SFB 649 Discussion Papers SFB649DP2010-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    22. Vladimir Panov, 2010. "Estimation of the signal subspace without estimation of the inverse covariance matrix," SFB 649 Discussion Papers SFB649DP2010-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    23. Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper series 59_12, Rimini Centre for Economic Analysis, revised Sep 2012.
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    25. 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).

    More about this item

    Keywords

    Term structure of interest rates; Stochastic volatility; Dynamic factor model; Markov chain Monte Carlo;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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