Bayesian inference in a Stochastic Volatility Nelson–Siegel model

Nikolaus Hautsch, Fuyu Yang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)3774-3792
Number of pages19
JournalComputational Statistics & Data Analysis
Volume56
Issue number11
DOIs
Publication statusPublished - Nov 2012

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