Xiaosai Liao, Xinjue Li, Qingliang Fan
arXiv 19 Sep 2024 · Econometrics
arXiv:2410.03557 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cochrane, J. H. and Piazzesi, M (2005) Bond Risk Premia | 1.000 | 5 | 3 | 100% |
| 2 | Ludvigson, S. C. and Ng, S (2009) Macro factors in bond risk premia | 1.000 | 5 | 3 | 100% |
| 3 | Lee, J (2016) Predictive Quantile Regression with Persistent Covariates: IVX-QR Approach | 0.860 | 11 | 4 | 64% |
| 4 | Cieslak, A. and Povala, P (2015) Expected Returns in Treasury Bonds | 0.843 | 3 | 3 | 100% |
| 5 | Liao, X., Li, X., and Fan, Q (2024) Robust Inference for Multiple Predictive Regressions with an Application on Bond Risk Premia self | 0.754 | 14 | 4 | 43% |
| 6 | Bauer, M. D. and Hamilton, J. D (2018) Robust Bond Risk Premia | 0.737 | 3 | 2 | 100% |
| 7 | Liu, X., Long, W., Peng, L., and Yang, B (2023) A Unified Inference for Predictive Quantile Regression | 0.693 | 6 | 3 | 33% |
| 8 | Ghysels, E., Horan, C., and Moench, E (2018) Forecasting through the Rearview Mirror: Data Revisions and Bond Return Predictability | 0.644 | 2 | 2 | 100% |
| 9 | Fan, R. and Lee, J (2019) Predictive Quantile Regressions under Persistence and Conditional Heteroskedasticity | 0.511 | 4 | 2 | 25% |
| 10 | Kostakis, A., Magdalinos, T., and Stamatogiannis, M (2015) Robust Econometric Inference for Stock Return Predictability | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 23 scored citations.