Method Development Aspects of Liquidity Risk Modelling: Dynamic Algorithms for Reinforcement Machine Learning Under Crisis Market Perspectives

Mazin A. M. Al Janabi

Chapter 3 in Corporate Risk Management after the COVID-19 Crisis, 2023, pp 65-93 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This chapter reviews the methodological development phases of Al Janabi’s (2012) optimisation algorithms for the assessment of Liquidity-Adjusted Value-at-Risk (LVaR) technique under crisis market outlooks. This chapter examines the different facets for the development of robust risk modelling techniques that attempt to address the problem of market/liquidity risk of multi-assets portfolios. The proposed theoretical foundations and optimisation algorithms have uncovered that better investable portfolios can be attained than using the conventional Markowitz (1952) portfolio theory. The modelling algorithms and optimisation techniques discussed in this chapter can aid in evolving risk and portfolio management practices, particularly in light of the consequences of the 2007–2009 financial crisis. In addition, the recommended risk management modelling techniques and optimisation algorithms can have key applications in reinforcement machine learning, expert systems, smart financial functions, Internet of Things (IoT), and financial technology (fintech) in big data ecosystems.

Keywords: COVID-19; Pandemic; Coronavirus; Small and Medium sized Business; SME; Risk Management; Keynesian Theory of Business Cycle; Social-Ecological Theory; Social Cognitive Theory; Nigeria; EU Artificial Intelligence Act; European Union; Artificial Intelligence; Labour relation; Fintech; Business Model; Economic Growth; Regulators; Policy Makers; Business; Corporate; Finance; AI; T-Shaped Teams; Al Janabi Model; Algorithms; Commodity; Crisis; Liquidity Risk; Internet of Things (IoT); Liquidity-Adjusted Value-at-Risk; Reinforcement Machine Learning; Optimization; Portfolio Management (search for similar items in EconPapers)
JEL-codes: G3 G32 (search for similar items in EconPapers)
Date: 2023
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