Table 6 In-sample predictability of Swiss bond market excess returns

Predictor

\(\hat {\beta }\)

R²(%)

\(\hat {\beta }\)

R²(%)

\(\hat {\beta }\)

R²(%)

\(\hat {\beta }\)

R²(%)

dp

0.08*

0.87

0.06

1.39

0.05

1.32

0.03

0.95

(p value)

(0.09)

(0.18)

(0.25)

(0.34)

dy

0.06

0.52

0.05

0.77

0.04

0.75

0.02

0.55

(p value)

(0.16)

(0.24)

(0.31)

(0.38)

ep

– 0.03

0.13

– 0.03

0.40

– 0.05

1.30

– 0.07

5.28

(p value)

(0.70)

(0.72)

(0.78)

(0.89)

de

0.07

0.54

0.06

1.19

0.06

2.29

0.07

5.93

(p value)

(0.17)

(0.16)

(0.13)

(0.08)

svar

0.19***

4.72

0.13***

5.45

0.07

2.96

0.05

2.90

(p value)

(0.00)

(0.00)

(0.02)

(0.07)

ntis

0.01

0.03

0.02

0.20

0.01

0.03

-0.00

0.00

(p value)

(0.43)

(0.41)

(0.48)

(0.51)

ts

0.18***

3.95

0.19***

12.18

0.21**

23.07

0.17**

33.22

(p value)

(0.01)

(0.01)

(0.01)

(0.01)

ds

0.11*

1.59

0.09*

2.42

0.06

2.20

0.05

2.96

(p value)

(0.06)

(0.09)

(0.16)

(0.13)

Panel B: marginal contribution of GVD^old in the presence of the best US predictor at each forecast horizon

\(\hat {\beta }\)

Partial R²(%)

\(\hat {\beta }\)

Partial R²(%)

\(\hat {\beta }\)

Partial R²(%)

\(\hat {\beta }\)

Partial R²(%)

GVD^old

0.10**

1.49

0.06

1.55

0.01

0.64

0.01

0.43

(p value)

(0.03)

(0.23)

(0.46)

(0.50)