Efficient Portfolio Optimization in the Multivariate Generalized Hyperbolic Framework

In this article, we apply the multivariate Generalized Hyperbolic (mGH) distribution to portfolio modelling, using Conditional Value at Risk as a risk measure. Exploiting the fact that portfolios whose constituents follow an mGH distribution are univariate GH distributed, we prove some results relating to measurement and decomposition of portfolio risk, and show how to efficiently tackle portfolio optimization. Moreover, we develop a robust portfolio optimization approach in the mGH framework, using Worst Case Conditional Value at Risk as a risk measure