# Table 10 In-sample predictability of stock and bond market excess returns

Stock market return
h=1h=3h=6h=12
Predictor$$\hat {\beta }$$R2(%)$$\hat {\beta }$$R2(%)$$\hat {\beta }$$R2(%)$$\hat {\beta }$$R2(%)
GVD250.190.230.331.570.41*4.200.48*10.00
(0.82) (1.55) (2.06) (2.66)
GVD750.40*1.000.51**3.820.57**8.290.59**14.77
(1.56) (2.40) (3.28) (4.02)
Bond market return
h=1h=3h=6h=12
Predictor$$\hat {\beta }$$R2(%)$$\hat {\beta }$$R2(%)$$\hat {\beta }$$R2(%)$$\hat {\beta }$$R2(%)
GVD250.060.440.030.350.030.580.020.65
(0.89) (0.53) (0.63) (0.52)
GVD750.020.030.020.090.030.640.030.94
(0.28) (0.29) (0.63) (0.60)
1. Notes: This table presents OLS estimates from univariate regressions ofh-month ahead Swiss stock and bond market returns on GVD constructed with data of the 25 (GVD25) or 75 (GVD75) largest firms in the Swiss stock market. The sample period runs from January 1999 to December 2017. We compute heteroskedasticity and autocorrelation robustt-statistics (in parentheses below the estimates) from a wild bootstrap procedure that tests the null hypothesis of $$\hat {\beta }^{h}=0$$ against the alternative that $$\hat {\beta }^{h}>0$$ because the regressors are defined in such a way that high values predict high excess returns. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively