Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios

摘要: We propose a model for optimizing structured portfolios with liquidity-adjusted Value-at-Risk (LVaR) constraints, whereby linear correlations between assets are replaced by the multivariate nonlinear dependence structure based on Dynamic Conditional Correlation t-copula modeling. Our portfolio optimization algorithm minimizes LVaR function under adverse market circumstances and multiple operational financial constraints. When we consider diversified of international stock commodity indices realistic scenarios, obtained results consistently show superiority our approach relative to other competing strategies including minimum-variance, risk-parity equally weighted allocations.