The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

The question of which factors determine corporate bonds pricing is investigated by analysing the spreads of eurobonds issued by major G-10 companies during the 1991–2001 period. Three main results emerge from the analysis. First, bond ratings appear as the most important determinant of yield spreads, with investors’ reliance on rating agencies judgments increasing over time. Second, the primary market efficiency and the expected secondary market liquidity are not relevant explanatory factors of spreads cross-sectional variability. Finally, rating agencies adopt a different, ‘through the cycle’, evaluation criteria of default risk with respect to the forward looking one adopted by bond investors.