Salience effect and yield curve

Abstract

This paper studies the influence of salience on nominal and real yield curves by introducing the salience effect in the Piazzesi and Schneider model (hereafter PS). We construct the salience values based on the expected consumption growth using U.S. data. We find that salience values are negatively correlated with the expected consumption growth rates. Based on U.S. data from 1960q1 to 2020q4, we find that the salience model can generate upward nominal and real yield curves within reasonable risk aversion coefficients (less than 10), as well as well-fitted average yields with actual data. The salience model compensates for the PS or recursive preference model’s inability to generate an upward nominal or real yield curve within reasonable risk aversion. Furthermore, we provide empirical support for model implications.

Keywords:

• Salience
• asset pricing
• yield curve

JEL CODE:

• E43
• E71
• G12
• G41

Disclosure statement

The authors report there are no competing interests to declare.

Notes

1 The “endowment effect” experiment: the experiment had two phases, in the first phase the experimenter was given a mug, and in the second phase the experimenter could exchange the mug for a pen of similar value. The “endowment effect” states that almost all experimenters do not choose to trade in the second phase. (Thaler 1980).

2 Malloy et al. (2009) and Hansen et al. (2008) applied the same assumptions.

3 High salience degree would increase the term spreads generated by the model. The purpose of setting $\delta \text{=0.9}$ is to show that the model could still generate high term spreads with less salience degree.

4 The correlation between the log salience weight and the expected consumption growth rate affects the shape of the yield curve. Subtracting a constant does not affect the correlation between the log salience weight and the expected consumption growth rate, therefore it does not affect conclusions.

5 We compared the effect of the tangent function and linear function, and found that the difference between them is little. The tangent function is chosen in this paper.

6 The figure is omitted since the space limitations.

7 PS (2006) calibrates the coefficients to β = 1.005 and γ = 59. The slight difference with the parameters calibrated in this paper is because the price or quantity indexes in the NIPA data used in 2006 are benchmarked to the 2000 index = 100, while the benchmark of the NIPA data used in this paper is made to the 2012 index = 100.

8 https://www.asc.ohio-state.edu/mcculloch.2/ts/ts.html.

9 One-period TIPS yields are not available, thus we only utilise the nominal term spreads.