Term Structure Forecasting: No-Arbitrage Restrictions versus Large Information Set
Term Structure Forecasting: No-Arbitrage Restrictions versus Large Information Set
复制标题
期限结构预测：无套利限制与大信息集
DOI：
10.1002/for.1181
复制
发表时间：
2012-03-01
影响因子：
3.4
通讯作者：
Sala, Luca
中科院分区：
文献类型：
--
作者：
Favero, Carlo A.;Niu, Linlin;Sala, Luca
本文讨论了预测期限结构的问题。我们提供了一个统一的状态空间建模框架，其中包含不同的现有离散时间收益率曲线模型。在这样的框架内，我们分析了两种建模选择对预测性能的影响，即施加无套利限制和用于提取因子的信息集的大小。使用美国收益率曲线数据，我们发现无套利和大量信息集都有助于预测，但没有一个模型能够统一主导另一个模型。无套利模型在期限较短、期限较短的情况下更有用。大型信息集在较长的时间范围和较长的期限内更有用。我们还发现了收益率曲线模型对宏观经济变量存在显着反馈的证据，可用于宏观经济预测。版权所有 (C) 2010 约翰·威利父子有限公司
This paper addresses the issue of forecasting term structure. We provide a unified state-space modeling framework that encompasses different existing discrete-time yield curve models. Within such a framework we analyze the impact of two modeling choices, namely the imposition of no-arbitrage restrictions and the size of the information set used to extract factors, on forecasting performance. Using US yield curve data, we find that both no-arbitrage and large information sets help in forecasting but no model uniformly dominates the other. No-arbitrage models are more useful at shorter horizons for shorter maturities. Large information sets are more useful at longer horizons and longer maturities. We also find evidence for a significant feedback from yield curve models to macroeconomic variables that could be exploited for macroeconomic forecasting. Copyright (C) 2010 John Wiley & Sons, Ltd.