The Economic Origin of Treasury Excess Returns: A Cycles and Trend Explanation

R. Rebonato, Takumi Hatano
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引用次数: 4

Abstract

In this paper we try to understand the economic explanation of the difference in predictability afforded by the old and the new-generation return-predicting factors. To do so, first we show that the Cieslak-Povala (2010) approach can be expressed in terms of a conditional prediction of where the level and the slope of the yield curve should be, given long-term inflation. We then explore whether this interpretation is valid, or whether, as Cochrane (2015) argues, the Cieslak-Povala factor simply owes its effectiveness to its acting as a de-trender. We answer this question by decomposing excess returns into low- and high-frequency components; by showing that the old and new return-predicting factors capture very different periodicities of the return power spectrum; and by showing that a high speed of mean-reversion is required for the high-frequency part of the spectrum. We conclude that creating strongly mean-reverting cycles is key to predicting excess returns effectively, and explore to what extent the Cieslak-Povala approach may be 'special' in this respect. We give a financial interpretation to the low- and high-frequency sources of excess returns, and, based on the understanding this decomposition affords, we show how to build almost by inspection a whole class of extremely parsimonious, robust and financially-motivated return-predicting factors which forecast in- and out-of-sample returns as well or better than factors built using many more variables.
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国债超额收益的经济根源:周期与趋势解释
在本文中,我们试图理解新旧两代收益预测因子所提供的可预测性差异的经济学解释。为此,我们首先表明,cieslake - povala(2010)方法可以用给定长期通胀的条件预测来表达,即收益率曲线的水平和斜率应该在什么位置。然后,我们探讨这种解释是否有效,或者是否像Cochrane(2015)所认为的那样,cieslake - povala因素仅仅将其有效性归功于其作为去趋势的作用。我们通过将超额收益分解为低收益和高频收益来回答这个问题;通过表明新旧回归预测因子捕获的回归功率谱的周期性非常不同;并且通过表明对于频谱的高频部分需要高速的均值回归。我们得出的结论是,创建强均值回归周期是有效预测超额回报的关键,并探讨了cieslake - povala方法在这方面的“特殊”程度。我们对低频率和高频率的超额收益来源进行了金融解释,并且,基于这种分解提供的理解,我们展示了如何几乎通过检验来构建一整类极其简洁、稳健和财务动机的回报预测因素,这些因素预测样本内外回报的效果与使用更多变量构建的因素一样好,甚至更好。
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