Risk Management in Emerging Markets in Post 2007–2009 Financial Crisis: Robust Algorithms and Optimization Techniques Under Extreme Events Market Scenarios

  • Mazin A. M. Al JanabiEmail author


This chapter implements a robust methodology for the evaluation, management, and control of market risk exposures for emerging financial trading portfolios that comprise illiquid equity assets. The simulation and testing approach are based on the renowned concept of Liquidity-Adjusted Value at Risk (LVaR) along with the application of optimization risk algorithm utilizing matrix-algebra techniques. Our broad market/liquidity risk technique and algorithms, by means of Al Janabi model (Madoroba and Kruger, Journal of Risk Model Validation, 8, 19–46, 2014), can simultaneously process and examine potential LVaR exposures under regular and intricate market outlooks. Furthermore, it can empirically test for the effects of illiquidity of traded equity securities under stressed market perspectives. With the purpose of demonstrating the appropriate use of LVaR and stress-testing methods, real-world examples, in the form of applied risk techniques and optimization case studies, along with quantitative analysis of trading risk management are presented for emerging Gulf Cooperation Council (GCC) stock markets. To that end, quite a few practical optimization case studies are accomplished with the objective of simulating a realistic framework of liquidity trading risk measurement, as well as to the application of a risk optimization process for the computation of upper limits LVaR risk budgeting.


Al Janabi model Big data Emerging markets Financial engineering Financial risk management FinTech GARCH-M (1,1) model GCC financial markets Global financial crisis Machine learning Portfolio management Stress testing Liquidity-adjusted value at risk 


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Copyright information

© The Author(s) 2020

Authors and Affiliations

  1. 1.EGADE Business SchoolTecnologico de MonterreyMexico CityMexico

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