Konferenzbeitrag

Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model

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In this paper, we develop and apply Bayesian inference for an extended Nelson-Siegel (1987) 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. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS model using simulation-based inference. Applying the SVNS model to monthly U.S. zero-coupon yields, we find significant evidence for time-varying volatility in the yield factors. This is mostly true for the level and slope volatility revealing also the highest persistence. It turns out that 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.

Language: Englisch

Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Computational Econometrics ; No. A3-V1

Classification: Wirtschaft
Bayesian Analysis: General
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

Subject: term structure of interest rates
stochastic volatility
dynamic factor

Event: Geistige Schöpfung

(who): Yang, Fuyu
Hautsch, Nikolaus

Event: Veröffentlichung

(who): Verein für Socialpolitik

(where): Frankfurt a. M.

(when): 2010

Handle: http://hdl.handle.net/10419/37145

Last update: 10.03.2025, 11:43 AM CET

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Object type

Konferenzbeitrag

Associated

Yang, Fuyu
Hautsch, Nikolaus
Verein für Socialpolitik

Time of origin

2010

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