Joint VaR and ES forecasting in a multiple quantile regression framework
An accurate assessment of tail dependencies of financial returns is key for risk management and portfolio allocation. In this paper we consider a multiple linear quantile regression setting for joint prediction of tail risk measures, namely Value at Risk(Var) and Expected Shortfall (ES) using a generalization of the Multivariare Asymmetric Laplace distribution. The proposed method permits simultaneous modelling of multiple conditional quantiles of a multivariate response variable and allows to study the dependence structure among financial assets at different quantile levels.