Bayesian inference in a Stochastic Volatility NelsonSiegel model
Bayesian inference is developed and applied for an extended NelsonSiegel term structure model capturing interest rate risk. The so-called Stochastic Volatility NelsonSiegel (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 models 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.
stochastic volatility markov chain monte carlo dynamic factor model term structure of interest rates