Robust Bond Risk Premia Predictability Test in the Quantiles
债券风险溢价可预测性的稳健分位数检验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究债券收益预测，并以条件分位数区分中心与尾部的预测关系。
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债券风险溢价可预测性的稳健分位数检验
- 研究问题
- 收益率曲线的 CP 因子是否在不同条件分位数上涵盖未来债券收益的全部可用信息，还是宏观变量提供额外预测能力？
- 方法与思路
- 作者在包含多个高度持续预测变量的分位数回归中提出稳健推断，加入债务持有趋势等变量，并比较样本内外预测。
- 材料陈述的贡献
- 摘要称检验尺寸与功效有所改善；1980—2022 年数据支持中心分位数的宏观涵盖假设，但尾部分位数出现反对证据。
- 阅读关注点
- 应核对持续性处理和多变量边际检验的理论条件、预测变量构造、分位数选择，以及样本外预测是否避免信息泄漏。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实；摘要未给出检验公式、显著性与预测改善数值，中心和尾部结论仅限所述样本。
原文摘要
Different from existing literature on testing the macro-spanning hypothesis of bond risk premia, which only considers mean regressions, this paper investigates whether the yield curve represented by CP factor (Cochrane and Piazzesi, 2005) contains all available information about future bond returns in a predictive quantile regression with many other macroeconomic variables. In this study, we introduce the Trend in Debt Holding (TDH) as a novel predictor, testing it alongside established macro indicators such as Trend Inflation (TI) (Cieslak and Povala, 2015), and macro factors from Ludvigson and Ng (2009). A significant challenge in this study is the invalidity of traditional quantile model inference approaches, given the high persistence of many macro variables involved. Furthermore, the existing methods addressing this issue do not perform well in the marginal test with many highly persistent predictors. Thus, we suggest a robust inference approach, whose size and power performance are shown to be better than existing tests. Using data from 1980-2022, the macro-spanning hypothesis is strongly supported at center quantiles by the empirical finding that the CP factor has predictive power while all other macro variables have negligible predictive power in this case. On the other hand, the evidence against the macro-spanning hypothesis is found at tail quantiles, in which TDH has predictive power at right tail quantiles while TI has predictive power at both tails quantiles. Finally, we show the performance of in-sample and out-of-sample predictions implemented by the proposed method are better than existing methods.