Evaluation of Optimal and Coherent Risk-Capital Structures Under Adverse Market Outlooks

Abstract

This paper broadens research literature associated with the assessment of modern portfolio risk management techniques by presenting a thorough modeling of nonlinear dynamic asset allocation and management under the supposition of illiquid and adverse market settings. This study analyses, from a fund manager’s perspective, the performance of liquidity adjusted risk modeling in obtaining optimal and coherent economic capital structures, subject to meaningful operational and financial constraints as specified by the fund manager. Specifically, the paper proposes a re-engineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both pure long positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ L-VaR matrix along with GARCH-M technique to forecast conditional volatility and expected return. The key methodological contribution is a different and less conservative liquidity scaling factor than the conventional root-t multiplier. Moreover, in this paper, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm which allocates both economic capital and trading assets by minimizing L-VaR subject to the constraints that the expected return, trading volume and liquidation horizon should meet the budget limits set by the fund manager. In addition, the paper illustrates how the modified L-VaR method can be used by an equity trading unit in a dynamic asset allocation framework for reporting risk exposure, optimizing economic capital, and assessing risk reduction alternatives. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on the L-VaR optimization procedure.<br><br><br>REFERENCES AND FURTHER READING:<br><br>Al Janabi, M.A.M., Ferrer, R., and Shahzad, S. J. H., (2019). “Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach”. Physica A: Statistical Mechanics and its Applications, Volume 536, Article 122579.<br><br>Al Janabi, M.A.M., Arreola-Hernández, Jose, Berger, Theo, Khuong Nguyen, Duc, (2017), “Multivariate Dependence and Portfolio Optimization Algorithms under Illiquid Market Conditions”, European Journal of Operational Research, Vol. 259, No. 3, pp. 1121-1131.<br><br>Al Janabi, M.A.M. (2021a), “Is Optimum Always Optimal? A Revisit of the Mean-Variance Method under Nonlinear Measures of Dependence and Non-Normal Liquidity Constraints”. Journal of Forecasting, Vol. 40, No. 3, pp. 387-415.<br><br>Al Janabi, M.A.M. (2021b), “Multivariate Portfolio Optimization under Illiquid Market Prospects: A Review of Theoretical Algorithms and Practical Techniques for Liquidity Risk Management”. Journal of Modelling in Management, Vol. 16, No. 1, pp. 288-309. <br><br>Al Janabi, M.A.M. (2014), “Optimal and Investable Portfolios: An Empirical Analysis with Scenario Optimization Algorithms under Crisis Market Prospects”, Economic Modelling, Vol. 40, pp. 369-381.<br><br>Al Janabi, M.A.M. (2015), “Scenario Optimization Technique for the Assessment of Downside-Risk and Investable Portfolios in Post-Financial Crisis”, Int. J. of Financial Engineering, Vol. 2, No. 3, pp. 1550028-1 to 1550028-28. <br><br>Al Janabi, M.A.M. (2013), “Optimal and Coherent Economic-Capital Structures: Evidence from Long and Short-Sales Trading Positions under Illiquid Market Perspectives”, Annals of Operations Research, Vol. 205, No. 1, pp. 109-139.<br><br>Al Janabi, M.A.M. (2012), “Optimal Commodity Asset Allocation with a Coherent Market Risk Modeling”, Review of Financial Economics, Vol. 21, No. 3, pp. 131-140.<br><br>Al Janabi, M.A.M. (2011), “A Generalized Theoretical Modeling Approach for the Assessment of Economic Capital under Asset Market Liquidity Risk Constraints”, Service Industries Journal, Vol. 31, No. 13 &amp; 14, pp. 2193-2221. <br><br>Al Janabi, M. A.M. (2008), “Integrating liquidity risk factor into a parametric value at risk Method”, Journal of Trading, Vol. 3, No. 3, pp. 76–87.<br><br>Arreola-Hernandez, J. and Al Janabi, M.A.M. (2020), “Forecasting of dependence, market and investment risks of a global index portfolio”. Journal of Forecasting, Vol. 39, No. 3, pp. 512-532.<br><br>Arreola-Hernandez, J., Hammoudeh, S., Khuong, N.D., Al Janabi, M.A.M., and Reboredo, J.C., (2017), “Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach,” Applied Economics, Vol. 49, No. 25, pp. 2409–2427.<br><br>Arreola-Hernandez, J., Al Janabi, M.A.M., Hammoudeh, S. and Nguyen, D.K. (2015), “Time lag dependence, cross-correlation and risk analysis of U.S. energy and non-energy stock portfolios,” Journal of Asset Management, Vol. 16, No. 7, pp. 467-483.<br><br>Asadi, S., and Al Janabi, M.A.M. (2020), “Measuring market and credit risk under Solvency II: Evaluation of the standard technique versus internal models for stock and bond markets”, European Actuarial Journal, Vol. 10, No. 2, pp. 425–456.<br><br>Grillini, S., Sharma, A., Ozkan, A., &amp; Al Janabi, M.A.M. (2019), “Pricing of time-varying illiquidity within the Eurozone: Evidence using a Markov switching liquidity-adjusted capital asset pricing model”. International Review of Financial Analysis, Vol. 64, pp. 145-158.<br><br>Uddin, M.S., Chi, G., Al Janabi, M.A.M., and Habib, T., (2020), “Leveraging random forest in micro-enterprises credit risk modeling for accuracy and interpretability”. International Journal of Finance &amp; Economics, Early View: https://doi.org/10.1002/ijfe.2346 <br>