In this paper, we apply the multivariate Generalized Hyperbolic (mGH) distribution to portfolio modelling, using Conditional Value at Risk (CVaR) 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 (WCVaR) as risk measure.
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Acknowledgements
SK gratefully acknowledges financial support from the German Research Council (DFG) and helpful discussions with Thomas Liebmann.