Bayesian inference in a Stochastic Volatility Nelson–Siegel model

Nikolaus Hautsch and Fuyu Yang

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3774-3792

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.

Keywords: Term structure of interest rates; Stochastic volatility; Dynamic factor model; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (15)

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Working Paper: Bayesian inference in a stochastic volatility Nelson-Siegel Model (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3774-3792

DOI: 10.1016/j.csda.2010.07.003

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