Working Paper: NBER ID: w9458
Authors: Michael W. Brandt; Amir Yaron
Abstract: We present an econometric procedure for calibrating no-arbitrage term structure models in a way that is time-consistent and robust to measurement errors. Typical no-arbitrage models are time-inconsistent because their parameters are assumed constant for pricing purposes despite the fact that the parameters change whenever the model is recalibrated. No-arbitrage models are also sensitive to measurement errors because they fit exactly each potentially contaminated bond price in the cross-section. We overcome both problems by evaluating bond prices using the joint dynamics of the factors and calibrated parameters and by locally averaging out the measurement errors. Our empirical application illustrates the trade-off between fitting as well as possible and overfitting the cross-section of bond prices due to measurement errors. After optimizing this trade-off, our approach fits almost exactly the cross-section of bond prices at each date and produces out-of-sample forecast errors that beat a random walk benchmark and are comparable to the results in the affine term structure literature. We find that non-linearities in the pricing kernel are important, lending support to quadratic term structure models.
Keywords: no-arbitrage models; term structure; bond pricing; econometric procedure
JEL Codes: G0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| traditional no-arbitrage models are time inconsistent (G19) | errors in the pricing of bonds (G12) |
| recalibrating traditional models (C59) | time inconsistency of models (D15) |
| proposed econometric procedure resolves inconsistency (C51) | more accurate estimation of dynamics (C69) |
| separating exogenous factors from preference parameters (C51) | more accurate estimation of dynamics (C69) |
| rolling sample estimator averages out measurement errors (C22) | mitigates sensitivity of no-arbitrage models to measurement errors (C58) |
| method produces out-of-sample forecast errors that outperform a random walk benchmark (C53) | more reliable cross-sectional fit of bond prices (G12) |
| importance of nonlinearities in the pricing kernel (G19) | support for quadratic term structure models (C22) |