Bayesian inference in a Stochastic Volatility Nelson–Siegel model
AbstractBayesian 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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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 11 ()
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Web page: http://www.elsevier.com/locate/csda
Term structure of interest rates; Stochastic volatility; Dynamic factor model; Markov chain Monte Carlo;
Other versions of this item:
- Nikolaus Hautsch & Fuyu Yang, 2010. "Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model," SFB 649 Discussion Papers SFB649DP2010-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- 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
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