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publications feature a motif taken from the €5 banknote. NOTE: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. Macroprudential Research Network This paper presents research conducted within the Macroprudential Research Network (MaRs). The network is composed of economists from the European System of Central Banks (ESCB), i.e. the national central banks of the 27 European Union (EU) Member States and the European Central Bank. The objective of MaRs is to develop core conceptual frameworks, models and/or tools supporting macro-prudential supervision in the EU. The research is carried out in three work streams: 1) Macro-financial models linking financial stability and the performance of the economy; 2) Early warning systems and systemic risk indicators; 3) Assessing contagion risks.

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This paper aims to measure the independence of bank supervisory agencies and its effects on banking system soundness. Building on a new dataset, it first examines legal bank supervisor independence across OECD countries. Central-bank supervisors tend to be more independent than separate supervisory agencies. Moreover, the higher bank supervisor independence, the less integrated financial sector supervisors. The empirical analysis then suggests that independent bank supervisors have a higher credibility in banking markets, and market reaction to higher independence is reflected in higher ratings of banking system soundness. The effect strengthens at high levels of adherence to the rule of law. In contrast, legal independence from industry is found positively but not robustly related to the health of banking systems, suggesting that supervisory capture is not necessarily destabilising.

Abstract The tail dependence of multivariate distributions is frequently studied via the tool of copulas. This paper develops a general method to evaluate the tail dependence of heavy-tailed scale mixtures of multivariate distributions whose copulas are not explicitly accessible. The tractable formulas of tail dependence indexes are derived and the monotonicity properties of these indexes with respect to the distribution parameters are established. The multivariate elliptical distribution and multivariate Pareto distribution are discussed to illustrate the results.

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contagion and optimal regulatory forbearance

The tail dependence of multivariate distributions is frequently studied via the tool of copulas. This paper develops a general method, which is based on multivariate regular variation, to evaluate the tail dependence of heavy-tailed scale mixtures of multivariate distributions, whose copulas are not explicitly accessible. Tractable formulas for tail dependence parameters are derived, and a sufficient condition under which the parameters are monotone with respect to the heavy-tail index is obtained. The multivariate elliptical distributions are discussed to illustrate the results. Key words and phrases: Tail dependence, heavy tail, multivariate regular variation, copula, hazard rate ordering, multivariate elliptical distribution. MSC2000 classification: 62H20, 62P05. 1

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