Computation of Coherent Economic-Capital Structures with Machine Learning Techniques
4 Pages Posted: 27 Apr 2021 Last revised: 1 May 2021
Date Written: April 28, 2021
This research 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. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios using machine learning techniques, 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. 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. 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.
Keywords: Economic Capital, Emerging Markets, GARCH, GCC Financial Markets, Liquidity-Adjusted Value at Risk, Liquidity Risk, Machine Learning, Portfolio Management, Risk Management
JEL Classification: C10, C13, G20, G28
Suggested Citation: Suggested Citation