A Worst-Case Risk Measure by G-VaR

A kind of worst-case value-at-risk, GVaR, is defined to measure risk incorporating model uncertainty. Compared with most extant notions of worst-case VaR, GVaR can be computed by an explicit formula, and can be applied to large portfolios of several hundreds dimensions with low computational cost. It is robust for, but not limited to a set of VaRs based on normal distributions. We also reveal connections to robust portfolio optimization, which provides a tractable way to give optimal allocations under market ambiguity. Empirical analysis demonstrates that GVaR is a reliably robust risk measure