ABSTRACT
This chapter extends research literature associated with modern portfolio risk management techniques, by presenting robust modeling algorithms for nonlinear dynamic asset allocation and management under extreme events of illiquidity and adverse market perspectives. This research study examines, from portfolio managers’ perspective, the performance of liquidity-adjusted risk modeling in obtaining optimum and coherent economic capital structures, subject to the application of meaningful operational and financial constraints. Specifically, the chapter examines robust quantitative modeling methods for optimum economic capital allocation, in a liquidity-adjusted value at risk (L-VaR) framework, particularly from the perspective of trading portfolios that have both long- and short-sale trading positions or those portfolios that consist of long-only holdings. This chapter expands earlier attempts by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple-asset L-VaR matrix along with the application of the GARCH-M (1,1) technique to forecast conditional volatilities and expected returns. The key methodological contribution is a different and less conservative liquidity-scaling factor than the conventional root-t multiplier. Moreover, in this chapter, we develop a dynamic nonlinear portfolio selection model and optimization algorithms that allocate both economic capital and trading assets by minimizing the objective function of L-VaR. The optimization process is devised in a way that satisfies the objective function constraints of expected returns, trading volumes, and liquidation horizons set by the portfolio manager. The modeling algorithms are interesting in terms of theory as well as for key practical applications and can have many uses and applications in financial markets, especially in the wake of the 2007–2009 global financial meltdown. Furthermore, the proposed computational techniques can have key uses and applications in machine learning and artificial intelligence, expert systems, smart financial functions, Internet of things (IoT), and financial technology (FinTech) in big data environments.
