Bayesian Inference in a Stochastic Volatility Nelson–Siegel Model
Bayesian Inference in a Stochastic Volatility Nelson–Siegel Model
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DOI：
10.1016/j.csda.2010.07.003
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
N. Hautsch;Fuyu Yang
中科院分区：
文献类型：
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作者：
N. Hautsch;Fuyu Yang
贝叶斯推理被开发并应用于捕获利率风险的扩展 Nelson-Siegel 期限结构模型。所谓的随机波动尼尔森-西格尔（SVNS）模型允许基础收益率因子存在随机波动。提出了马尔可夫链蒙特卡罗 (MCMC) 算法，以使用基于仿真的推理来有效地估计 SVNS 模型。 SVNS 模型适用于美国零息票月度收益率。发现了收益率因子随时间变化的波动性的重要证据。纳入随机波动率可以提高模型的拟合优度，并明显降低预测的不确定性，特别是在低波动率时期。所提出的方法被证明是有效的，并且很容易适应揭示（多元）随机波动性的动态因子模型的替代规范。
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.