Deconstructing the Yield Curve

Richard K. Crump, Nikolay Gospodinov
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引用次数: 11

Abstract

We investigate the factor structure of the term structure of interest rates and argue that characterizing the minimal dimension of the data generating process is more challenging than currently appreciated. As a result, inference procedures for yield curve models that commit to a parsimoniously parameterized factor structure may be omitting important information about the underlying true factor space. To circumvent these difficulties, we introduce a novel nonparametric bootstrap that is robust to general forms of time and cross-sectional dependence and conditional heteroskedasticity of unknown form. We show that our bootstrap procedure is asymptotically valid and exhibits excellent finite-sample properties in simulations. We demonstrate the applicability of our results in two empirical exercises: first, we show that measures of equity market tail risk and the state of the macroeconomy predict bond returns beyond the level or slope of the yield curve; second, we provide a bootstrap-based bias correction and confidence intervals for the probability of recession based on the shape of the yield curve. Our results apply more generally to all assets with a finite maturity structure.
解构收益率曲线
我们研究了利率期限结构的因素结构,并认为表征数据生成过程的最小维度比目前所认识的更具挑战性。其结果是,收益率曲线模型的推理程序承诺一个简约的参数化的因素结构可能会忽略有关潜在的真实因素空间的重要信息。为了克服这些困难,我们引入了一种新的非参数自举,它对一般形式的时间和截面依赖以及未知形式的条件异方差具有鲁棒性。我们在模拟中证明了我们的自举过程是渐近有效的,并表现出良好的有限样本性质。我们通过两个实证练习证明了我们的结果的适用性:首先,我们表明股票市场尾部风险和宏观经济状况的指标预测债券回报超出了收益率曲线的水平或斜率;其次,我们根据收益率曲线的形状提供了基于自启动的偏差校正和衰退概率的置信区间。我们的结果更普遍地适用于所有有限期限结构的资产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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