What Goliaths and Davids among Swiss firms tell us about expected returns on Swiss asset markets

Swiss Journal of Economics and Statistics

Table 1 Descriptive statistics and variance decomposition: GVD and its components

Panel A: Descriptive statistics
	Mean	Std Dev	Max	Min	Auto correlation
GVD	– 0.195	0.030	0.051	– 0.156	0.880
GVD^new	– 0.173	0.027	0.035	– 0.130	0.838
GVD^old	– 0.022	0.013	0.055	– 0.045	0.906
Panel B: Variance decomposition
		\(\frac {\mathrm {{var}(GVD^{old})}}{\mathrm {var(GVD)}}\)	\(\frac {\mathrm {var(GVD^{new})}}{\mathrm {var(GVD)}}\)	\(2\frac {\mathrm {cov(GVD^{old},GVD^{new})}}{\mathrm {var(GVD)}}\)
	Share explained	0.205	0.828	– 0.033

Notes: Panel A of this table presents descriptive statistics of GVD which is defined as the change in the weight of the L largest firms in the aggregate Swiss stock market from t−Δt to t. Here, Δt is 12 months and L=50. GVD^old measures that component of GVD that is due to changes in the prices of existing capital of the L largest firms relative to the aggregate market from t−12 to t. GVD^new measures that component of GVD that is due to changes in the market capitalization of the L largest firms relative to the aggregate market from t−12 to t due to capital raising or capital decreasing activities such as the issuance of new shares or share buybacks. Panel B presents the variance decomposition of GVD into the parts driven by GVD^old, GVD^new, and the covariance between GVD^old and GVD^new. The sample period runs from January 1999 to December 2017