Abstract
Portfolio management and integrated risk management are more commonly applied toward enterprise risk management, requiring multivariate risk measures that capture the dependence among many risk factors. In this paper we propose the non-parametric estimator for multivariate value at risk (MVaR) and multivariate average value at risk (MAVaR) based on the multivariate geometric quantile approach and derive the symptotic properties of the proposed estimators for MVaR. We also present their performances under both simulated data and high-frequency financial data from the New York Stock Exchange. In addition, we compare our method with the delta normal approach and order statistics approach. The overall empirical results confirm that the multivariate geometric quantile approach significantly improves the risk management performance of MVaR and MAVaR.
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