Adaptive Lasso for vector Multiplicative Error Models

In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), and we show that they provide asymptotic consistency in variable selection and the same efficiency as if the set of true predictors were known in advance (oracle property). A Monte Carlo exercise demonstrates the good performance of this approach and an empirical application shows its effectiveness in studying the network of volatility spillovers among European financial indices, during and after the sovereign debt crisis. We conclude demonstrating the superior volatility forecast ability of Adaptive Lasso techniques also when a common trend is removed prior to multivariate volatility spillover analysis.