Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management

What is it about?

In this paper, we examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of Liquidity-Adjusted Value-at-Risk (LVaR), to multivariate optimization of investment portfolios. The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within expert systems & smart financial applications, financial technology (FinTech), machine learning, and big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk.

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