Bayesian inference in a stochastic volatility Nelson–Siegel model

Hautsch, Nikolaus and Yang, Fuyu (2012) Bayesian inference in a stochastic volatility Nelson–Siegel model. Computational Statistics & Data Analysis, 56 (11). pp. 3774-3792.

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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.

Item Type: Article
Faculty \ School: Faculty of Social Sciences > School of Economics
UEA Research Groups: Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance
Depositing User: Julia Sheldrake
Date Deposited: 19 Jan 2011 17:12
Last Modified: 21 Jul 2024 16:30
DOI: 10.1016/j.csda.2010.07.003

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