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Bayesian inference in a Stochastic Volatility Nelson–Siegel model

  • Hautsch, Nikolaus
  • Yang, Fuyu

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S0167947310002768
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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 11 ()
Pages: 3774-3792

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Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3774-3792
DOI: 10.1016/j.csda.2010.07.003
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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