Matteo Iacopini, Aubrey Poon, Luca Rossini, Dan Zhu
arXiv 18 Jan 2024 · Statistics — Applications
arXiv:2401.09874 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel framework for modeling the yield curve from a quantile perspective. Building on the dynamic Nelson-Siegel model of Diebold et al. (2006), we extend its traditional mean-based approach to a quantile regression setting, enabling the estimation of yield curve factors - level, slope, and curvature - at specific quantiles of the conditional distribution. A key advantage of our framework is its ability to characterize the entire conditional distribution of the yield curve across maturities and over time. In an empirical analysis of the U.S. term structure of interest rates, our method demonstrates superior out-of-sample forecasting performance, particularly in capturing the tails of the yield distribution - an aspect increasingly emphasized in the recent literature on distributional forecasting. In addition to its forecasting advantages, our approach reveals rich distributional features beyond the mean. In particular, we find that the dynamic changes in these distributional features differ markedly between the Great Recession and the COVID-19 pandemic period, highlighting a fundamental shift in how interest rate markets respond to distinct economic shocks.
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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 | Diebold, F. X., G. D. Rudebusch, and S. B. Aruoba (2006) The macroeconomy and the yield curve: a dynamic latent factor approach | 1.000 | 10 | 3 | 100% |
| 2 | Diebold, F. X. and C. Li (2006) Forecasting the term structure of government bond yields | 0.928 | 4 | 3 | 100% |
| 3 | Hansen, P. R., A. Lunde, and J. M. Nason (2011) The model confidence set | 0.811 | 4 | 2 | 100% |
| 4 | Kotz, S., T. Kozubowski, and K. Podgórski (2001) The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance | 0.811 | 4 | 2 | 100% |
| 5 | Coroneo, L., D. Giannone, and M. Modugno (2016) Unspanned macroeconomic factors in the yield curve | 0.737 | 3 | 2 | 100% |
| 6 | Carriero, A., T. E. Clark, and M. Marcellino (2022) Nowcasting tail risk to economic activity at a weekly frequency | 0.644 | 2 | 2 | 100% |
| 7 | Chan, J. C. and I. Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models | 0.644 | 2 | 2 | 100% |
| 8 | Chan, J. C (2020) Large Bayesian vector autoregressions | 0.644 | 2 | 2 | 100% |
| 9 | Chan, J. C., E. Eisenstat, and R. W. Strachan (2020) Reducing the state space dimension in a large TVP-VAR | 0.644 | 2 | 2 | 100% |
| 10 | Koenker, R. and G. Bassett (1978) Regression quantiles | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 35 scored citations.