Efficient and robust portfolio optimization in the multivariate Generalized Hyperbolic framework

Stefan Kassberger

37 citations0/0 files storedQuantitative Finance

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Risk and Portfolio Optimization Stochastic processes and financial applications Financial Risk and Volatility Modeling

Abstract

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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References

1999Coherent Measures of Risk 2000Optimization of conditional value-at-risk 1952Portfolio Selection 2002Conditional value-at-risk for general loss distributions 2003Financial Modelling with Jump Processes 2010Theory and Applications of Robust Optimization 2005Quantitative Risk Management: Concepts, Techniques, and Tools 2002Correlation and Dependence in Risk Management: Properties and Pitfalls

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