利用调查预期锚定收益率曲线

Carlo Altavilla, R. Giacomini, Giuseppe Ragusa
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引用次数: 37

摘要

利率期限结构的动态行为很难用模型来复制,即使是具有实证表现记录的模型,自21世纪初以来也表现不佳。另一方面,调查预期是收益率的准确预测指标,但仅适用于非常短的期限。我们认为,这部分是由于调查参与者有能力将有关当前经济状况的信息以及前瞻性信息(如货币政策公告中包含的信息)纳入其中。我们展示了如何利用调查预期对短期收益率的信息优势来提高基本模型给出的收益率曲线预测的准确性。为此,我们采用了一种灵活的预测方法,将模型预测锚定在收益率曲线中存在信息优势的部分的调查预期上,并将优越的预测能力传递给所有剩余的收益率。该方法隐含地将调查参与者可以获得的任何信息纳入收益率曲线预测,而无需显式建模。我们的研究表明,相对于金融和政策机构广泛采用的预测利率期限结构的模型,锚定在整个收益率曲线的预测准确性方面取得了巨大而显著的进步,在2000年至2012年期间,锚定模型的准确率提高了52%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anchoring the Yield Curve Using Survey Expectations
The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to incorporate information about the current state of the economy as well as forward-looking information such as that contained in monetary policy announcements. We show how the informational advantage of survey expectations about short yields can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible projection method that anchors the model forecasts to the survey expectations in segments of the yield curve where the informational advantage exists and transmits the superior forecasting ability to all remaining yields. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy for the whole yield curve, with improvements of up to 52% over the years 2000-2012 relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.
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