Is optimum always optimal? A revisit of the mean-variance method under nonlinear measures of dependence and non-normal liquidity constraints

We develop a model for optimizing multiple-asset portfolios with semi-parametric liquidity-adjusted value-at-risk (LVaR), whereby linear correlations are substituted by the multivariate nonlinear Kendall's tau dependence measure, under multiple credible operational and budget constraints. When considering a diversified large portfolio of international stock markets of both developed and emerging economies and commodities, under both regular and stressed market perspectives, the obtained results consistently confirm the dominance of our modeling algorithms relative to other competing portfolio strategies, including the traditional mean-variance VaR approach and global minimum-variance portfolios. The obtained results are robust to different trading scenarios and promising for practical optimization techniques in large multiple-asset portfolios and operation research models in financial institution management.