We explore the importance of information hidden from the yield curve and assess how valuable the unspanned risks are to a real-time Bayesian investor seeking to forecast excess bond returns and maximise her utility. We propose a novel class of arbitrage-free unspanned Dynamic Term Structure Models (DTSM), that embed a stochastic market price of risk specification. We develop a suitable Sequential Monte Carlo (SMC) inferential and prediction scheme that guarantees joint identification of parameters and latent states and takes into account all relevant uncertainties. We find that latent factors contain significant predictive power above and beyond the yield curve, providing improvement to the out-of-sample predictive performance of models, especially at shorter maturities. Most importantly, they are capable of exploiting information hidden from the yield curve and translate the evident statistical predictability into significant utility gains, out-of-sample. The hidden component associated with slope risk is countercyclical and links with real activity.
翻译:我们探索了从收益曲线中隐藏的信息的重要性,评估了未披露的风险对于一个实时的贝叶斯投资者的价值,该投资者试图预测超额债券回报并最大限度地发挥她的效用。我们提出了新型的无套利无套的动态时间结构模型(DTSM ), 其中包含一个风险规格的随机市场价格。我们制定了一个适当的序列蒙特卡洛(SMC)推算和预测计划,保证联合确定参数和潜在状态,并考虑到所有相关的不确定性。我们发现潜在因素含有超出收益曲线的显著预测力,改善了模型的超模性预测性性能,特别是在较短的成熟期。最重要的是,它们能够利用从收益曲线中隐藏的信息,并将明显的统计可预测性转化为显著的效用收益。与斜坡风险相关的隐藏部分是反周期的,与实际活动相关联的。