Table 5 In-sample predictability of Swiss stock market excess returns: the US predictors versus GVD

Predictor

\(\hat {\beta }\)

R²(%)

\(\hat {\beta }\)

R²(%)

\(\hat {\beta }\)

R²(%)

\(\hat {\beta }\)

R²(%)

dp

0.27

0.48

0.36

1.88

0.46

5.36

0.52*

11.52

(p value)

(0.23)

(0.19)

(0.11)

(0.06)

dy

0.40

1.03

0.44

2.76

0.53*

6.92

0.55**

13.03

(p value)

(0.12)

(0.12)

(0.06)

(0.05)

ep

0.23

0.34

0.13

0.26

0.08

0.16

0.16

1.07

(p value)

(0.32)

(0.42)

(0.45)

(0.38)

de

– 0.07

0.03

0.05

0.04

0.14

0.52

0.10

0.45

(p value)

(0.52)

(0.43)

(0.35)

(0.36)

svar

– 0.67***

2.83

– 0.66***

6.47

– 0.31

2.38

0.01

0.00

(p value)

(0.00)

(0.00)

(0.25)

(0.45)

ntis

0.17

0.19

0.25

0.91

0.24

1.38

0.21

1.88

(p value)

(0.35)

(0.32)

(0.36)

(0.37)

ts

– 0.27

0.48

– 0.18

0.47

– 0.06

0.10

0.22

2.05

(p value)

(0.83)

(0.70)

(0.55)

(0.25)

ds

– 0.41

1.06

– 0.23

0.80

– 0.00

0.00

0.15

0.96

(p value)

(0.78)

(0.60)

(0.48)

(0.32)

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

\(\hat {\beta }\)

Partial R²(%)

\(\hat {\beta }\)

Partial R²(%)

\(\hat {\beta }\)

Partial R²(%)

\(\hat {\beta }\)

Partial R²(%)

GVD^new

0.54*

1.78

0.55**

4.58

0.51**

3.80

0.39*

4.31

(p value)

(0.07)

(0.04)

(0.04)

(0.10)