We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no-arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no-change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no-arbitrage affine term structure model's requirements that yields obey a factor structure and that the factors follow a Markov process. In the term structure model, its cross-equation no-arbitrage restrictions on the factor loadings appear to play a marginal role in forecasting gains.

中文翻译：

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无套利先验、浮动波动率和利率期限结构

我们使用具有随机波动率的贝叶斯向量自回归来预测政府债券收益率。我们从无套利仿射期限结构模型形成共轭先验。该模型提高了来自不变随机游走和具有随机波动率的仿射期限结构模型的点和密度预测的准确性。我们提出的方法可能会通过放宽无套利仿射期限结构模型的要求而成功，该模型的收益率服从因子结构并且因子遵循马尔可夫过程。在期限结构模型中，其对因子载荷的交叉方程无套利限制似乎在预测收益方面发挥了边际作用。