Working Paper: CEPR ID: DP10104
Authors: Antonio Gargano; Davide Pettenuzzo; Allan G. Timmermann
Abstract: Studies of bond return predictability find a puzzling disparity between strong statistical evidence of return predictability and the failure to convert return forecasts into economic gains. We show that resolving this puzzle requires accounting for important features of bond return models such as time varying parameters and volatility dynamics. A three-factor model comprising the Fama-Bliss (1987) forward spread, the Cochrane-Piazzesi (2005) combination of forward rates and the Ludvigson-Ng (2009) macro factor generates notable gains in out-of-sample forecast accuracy compared with a model based on the expectations hypothesis. Importantly, we find that such gains in predictive accuracy translate into higher risk-adjusted portfolio returns after accounting for estimation error and model uncertainty, as evidenced by the performance of model combinations. Finally, we find that bond excess returns are predicted to be significantly higher during periods with high inflation uncertainty and low economic growth and that the degree of predictability rises during recessions.
Keywords: Bayesian estimation; bond returns; model uncertainty; stochastic volatility; time-varying parameters
JEL Codes: G11; G12; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| time-varying parameters and stochastic volatility (C58) | predictive accuracy of bond returns (G12) |
| predictive accuracy of bond returns (G12) | higher risk-adjusted portfolio returns (G11) |
| economic conditions (recessions) (E32) | predictability of bond returns (G12) |
| higher risk-adjusted portfolio returns (G11) | economic outcomes (higher returns) (D29) |
| bond excess returns (G12) | predictability of bond returns during recessions (G12) |