No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates
提出一种结合无套利期限结构先验的贝叶斯向量自回归模型，用于预测政府债券收益率，在点预测和密度预测上优于随机游走和传统无套利模型。
Abstract 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.