标题： 关于动态期限结构模型中的非冗余潜在风险 显示英文标题

标题： On Unspanned Latent Risks in Dynamic Term Structure Models

摘要： 我们研究了隐藏在收益率曲线中的信息的重要性，并评估了未覆盖风险对于实时贝叶斯投资者预测超额债券回报并最大化其效用的价值。 我们提出了一类新的无套利未覆盖动态期限结构模型（DTSM），该模型嵌入了随机市场风险价格规范。 我们开发了一个合适的序贯蒙特卡罗（SMC）推理和预测方案，保证参数和潜在状态的联合识别，并考虑所有相关不确定性。 我们发现潜在因子在收益率曲线之外具有显著的预测能力，提高了模型的样本外预测性能，特别是在较短期限内。 最重要的是，它们能够利用隐藏在收益率曲线中的信息，并将明显的统计预测性转化为显著的样本外效用收益。 与斜率风险相关的隐藏成分具有逆周期特性并与实际活动相关联。 显示英文摘要

摘要： 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.