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Local-momentum autoregression and the modeling of interest rate term structure

机译:局部动量自回归与利率期限结构建模

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A parsimonious autoregressive model that is globally mean-reverting but locally driven by momentum is proposed. The local-momentum autoregression (LM-AR) model carries one extra parameter, and depending on the sign of this extra parameter, it can be either local momentum-preserving or momentum-building. The LM-AR model is motivated by observing US interest rate movement over many decades, which over a long time span seems to mean revert but over a period of several months or years can actually exhibit a momentum-like behavior. We use the LM-AR model with a stochastic central tendency factor as the dominant global risk factor in interest rates and add a local variation component of the standard mean-reverting type to create a 3-factor risk environment. We then derive its corresponding term structure model and empirically implement the model on US interest rates of seven maturities from January 1954 to December 2013 on a weekly frequency to establish the presence of local momentum building. (C) 2016 Elsevier B.V. All rights reserved.
机译:提出了一种全局自回归但局部受动量驱动的简约自回归模型。局部动量自回归(LM-AR)模型带有一个附加参数,根据该附加参数的符号,它可以是局部动量保持或动量建立。 LM-AR模型是通过观察数十年来美国的利率变动来推动的,在很长的一段时间内,这似乎意味着恢复原状,但在几个月或几年的时间里,实际上可能表现出类似动量的行为。我们将LM-AR模型与随机的中心趋势因子一起用作利率中的主要全局风险因子,并添加标准均值回复类型的局部变量成分以创​​建三因子风险环境。然后,我们推导其相应的期限结构模型,并以每周一次的频率经验性地执行从1954年1月至2013年12月的美国七个到期利率的模型,以建立当地动量模型。 (C)2016 Elsevier B.V.保留所有权利。

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