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

In: Corporate Risk Management after the COVID-19 Crisis

Author

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  • Mazin A. M. Al Janabi

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.

Suggested Citation

  • Mazin A. M. Al Janabi, 2023. "Method Development Aspects of Liquidity Risk Modelling: Dynamic Algorithms for Reinforcement Machine Learning Under Crisis Market Perspectives," World Scientific Book Chapters, in: Suman Lodh & Monomita Nandy (ed.), Corporate Risk Management after the COVID-19 Crisis, chapter 3, pages 65-93, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781800614239_0003
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