Working Paper: NBER ID: w27266
Authors: Yan Liu; Jing Cynthia Wu
Abstract: The constant-maturity zero-coupon Treasury yield curve is one of the most studied datasets. We reconstruct the yield curve using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yield curve. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gürkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data.
Keywords: yield curve; nonparametric; kernel smoothing; bond return forecasting; excess volatility
JEL Codes: E43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| new dataset (Y10) | bond return forecasting (G17) |
| new dataset (Y10) | excess volatility analyses (C58) |
| nonparametric kernel-smoothing method (C14) | pricing errors (D49) |
| choice of yield curve data (Y10) | conclusions in bond return forecasting (G17) |
| fourth and fifth principal components (C38) | predictive power for bond returns (G12) |