Bayesian Inference in a Stochastic Volatility Nelson–Siegel Model
Computational Statistics & Data Analysis, Forthcoming
19 Pages Posted: 25 Aug 2010
Date Written: July 5, 2010
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
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