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

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

Panel A: Descriptive statistics
 MeanStd DevMaxMinAuto correlation
GVD– 0.1950.0300.051– 0.1560.880
GVDnew– 0.1730.0270.035– 0.1300.838
GVDold– 0.0220.0130.055– 0.0450.906
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 explained0.2050.828– 0.033 
  1. 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 tΔt to t. Here, Δt is 12 months and L=50. GVDold 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. GVDnew 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 GVDold, GVDnew, and the covariance between GVDold and GVDnew. The sample period runs from January 1999 to December 2017