No abstract

This work is intended to assess the contribution to systemic risk of major companies in the European stock market on a geographical basis. We use the EuroStoxx 50 Index as a proxy for the financial system and we rely on the CoVaR and -CoVaR risk measures to estimate the contribution of each European country belonging to the index to systemic risk. We also conduct the significance and dominance test to evaluate whether the systemic relevance of considered countries is statistically significant and to determine which nation exerts the greatest influence on the spreading of negative spillover effects on the entire economy. Our empirical results show that, for the period ranging from 2008 to 2017, all countries contribute significantly to systemic risk, especially in times of crisis and high volatility in the markets. Moreover, it emerges that France is the systemically riskiest country, followed by Germany, Italy, Spain and Netherlands.

No abstract

This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behaviour.

No abstract