We investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google’s PageRank). Using European banking data, we show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations as developed in this paper. The correlation based network measures complement currently available alternative systemic risk ranking methods based on book or market values. A further analytical investigation shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.

This paper analyzes a wide range of flexible drift and diffusion specifications of stochastic-volatility jump–diffusion models for daily S&P 500 index returns. We find that model performance is driven almost exclusively by the specification of the diffusion component whereas the drift specifications is of second-order importance. Further, the variance dynamics of non-affine models resemble popular non-parametric high-frequency estimates of variance, and their outperformance is mainly accumulated during turbulent market regimes. Finally, we show that jump diffusion models yield more reliable estimates for the expected return of variance swap contracts.