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

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Abstract

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.

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Notes

  1. 1.

    For other relevant literature on liquidity, asset pricing and portfolio choice and diversification one can refer as well to Angelidis and Benos (2006); Berkowitz (2000); Madhavan, Richardson, and Roomans (1997); Hisata and Yamai (2000); Le Saout (2002); Amihud, Mendelson, and Pedersen (2005); Takahashi and Alexander (2002); Cochrane (2005); and Meucci (2009), among others. Furthermore, within the copula technique, and particularly the vine copula approach, there were indeed very few studies in this respect and most of published research is still focused on the issue of transaction costs (i.e., bid-ask spreads). In particular, Weiß and Supper (2013) investigate the issue of forecasting liquidity-adjusted intraday VaR with vine copulas. In their paper, they propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by implementing Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both returns and bid-ask spreads from intraday data, they incorporate the measurement of commonalities in liquidity and co-movements of stocks and bid-ask spreads into the forecasting of three types of liquidity-adjusted intraday VaR.

  2. 2.

    The time-varying pattern of assets volatility has been widely recognized and modeled as a conditional variance within the GARCH framework, as originally developed by Engle (1982, 1995). Engle (1982) introduced a likelihood ratio test to ARCH effects and a maximum likelihood method to estimate the parameters in the ARCH model. This approach was generalized by Bollerslev (1986) and Engle and Kroner (1995). In fact, the generalized autoregressive conditional heteroskedasticity in mean, GARCH-M (1,1) model, is used in our empirical analysis for the estimation of expected return and conditional volatility for each of the time series variables.

  3. 3.

    If the purpose of the risk analysis is to investigate diverse stock market dependences and related risk management measure, then Asseti should be the mark-to-market prices of the individual stock market indices.

  4. 4.

    The concept of coherent (investable) market portfolios refers to rational financial portfolios that are subject to meaningful financial and operational constraints. In this sense, coherent market portfolios lie-off the efficient frontiers as defined by Markowitz (1952), and instead have logical and well-structured long-only and long- and short-sales asset allocation.

  5. 5.

    The mathematical approach presented herein is largely drawn from Al Janabi, 2012, 2013, Al Janabi, 2014, and Al Janabi et al., 2017 research papers.

  6. 6.

    It is important to note that Eq. (7.11) can be used to calculate LVaR for any time horizon subject to the constraint that the overall LVaR figure should not exceed at any setting the nominal exposure, in other words the total trading volume.

  7. 7.

    In fact, the concept of liquidity risk in financial markets and institutions can imply either the added transaction costs related to trading large quantities of a certain financial security, or it can deal with the ability to trade this financial asset without triggering significant changes in its market prices (see Roch & Soner, 2013, for further details and empirical analysis).

  8. 8.

    Optimization results of the upper limits of risk-budgeting with different correlation parameters are highlighted in bold throughout Tables 7.4, 7.5, 7.6, 7.7, 7.8, and 7.9.

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Authors and Affiliations

  1. EGADE Business School, Tecnologico de Monterrey, Mexico City, Mexico

    Mazin A. M. Al Janabi

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Correspondence to Mazin A. M. Al Janabi .

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  1. EGADE Business School, Mexico City, Mexico

    Rajagopal

  2. International Management Institute, Bhubaneswar, India

    Ramesh Behl

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Al Janabi, M.A.M. (2020). Risk Management in Emerging Markets in Post 2007–2009 Financial Crisis: Robust Algorithms and Optimization Techniques Under Extreme Events Market Scenarios. In: Rajagopal, Behl, R. (eds) Innovation, Technology, and Market Ecosystems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-23010-4_7

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