ABSTRACT
This chapter reviews the theoretical and practical foundations of liquidity-adjusted value-at-risk (LVaR) and its application to optimization algorithms for portfolio selection and management. In particular, the chapter discusses the implementation of a robust optimization technique to investment portfolio selection and within an LVaR GARCH-M (1,1) framework. The proposed optimization algorithm demonstrates that better investable portfolios can be obtained than using the traditional Markowitz’s (1952) mean-variance technique. Our theoretical risk techniques and practical applications have important implications for portfolio selection and asset management, mostly in light of the aftershocks of the most recent financial crisis. In short, the advantages of the theoretical and practical fundamentals of LVaR and its application to optimization algorithms for portfolio selection include (1) the proposed optimization algorithms can aid in advancing portfolio selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints; (2) better investable portfolios can be achieved compared to the classical Markowitz’s (1952) mean-variance portfolio selection approach; (3) the proposed modeling scheme and optimization algorithms can be implemented by portfolio managers and risk managers for the valuation of proper asset allocations of diverse portfolios under adverse and event market outlooks, and within the environments of big data and expert systems.
JEL Classifications: C10, C13, G20, and G28
ACM Classification: F.2.1, G.1.6, H.2.8, I.1.2, and I.6.5
