Bayesian Inference in a Stochastic Volatility Nelson–Siegel Model

Computational Statistics & Data Analysis, Forthcoming

19 Pages Posted: 25 Aug 2010

See all articles by Nikolaus Hautsch

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Fuyu Yang

Humboldt University of Berlin; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Date Written: July 5, 2010

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

Suggested Citation

Hautsch, Nikolaus and Yang, Fuyu, Bayesian Inference in a Stochastic Volatility Nelson–Siegel Model (July 5, 2010). Computational Statistics & Data Analysis, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1663663

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Fuyu Yang (Contact Author)

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK 10099
Germany

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE) ( email )

Spandauer Strasse 1
Berlin, D-10178
Germany

Register to save articles to
your library

Register

Paper statistics

Downloads
86
Abstract Views
535
rank
292,348
PlumX Metrics