The Perspective of Balancing the Economic Growth of Healthcare Systems and Environmental Prevention

Based on real situations that mankind is confronting with the difficult era; insufficiency in food supplies, natural disasters, epidemic, etc. The paper is to econometrically compute portfolio optimization and predict efficiency frontiers for solving the most sensible scenario to suggest a sustainable policy in the three important pillars such as the growth of economic systems, environmental management, and public healthcare. The main observations are annual time-series information between 2000 and 2017 and collected from three countries in ASEAN. Singapore, Thailand, and Malaysia are the target. Methodologically, this research is to apply the quantum mechanism and the wave function for clarifying a real data distribution; true mean, and standard deviation of the data. These outcomes are the initial raw material for the modern portfolio optimization (for short-run policies) and efficient frontier computation (for long-term policies). Empirically, the results show some exclusive issues that can be the help for managing feasible budget allocations fairly and sustainably.

The perspective of balancing economic growth healthcare system and environmental prevention the efficient budget for ASEAN-3 countries

Based on real situations that mankind is confronting with the difficult era; insufficiency in food supplies, natural disasters, epidemic, etc. The paper is to econometrically compute portfolio optimization and predict efficiency frontiers for solving the most sensible scenario to suggest a sustainable policy in the three important pillars such as the growth of economic systems, environmental management, and public healthcare. The main observations are annual time-series information between 2000 and 2017 and collected from three countries in ASEAN. Singapore, Thailand, and Malaysia are the target. Methodologically, this research is to apply the quantum mechanism and the wave function for clarifying a real data distribution; true mean, and standard deviation of the data. These outcomes are the initial raw material for the modern portfolio optimization (for short-run policies) and efficient frontier computation (for long-term policies). Empirically, the results show some exclusive issues that can be the help for managing feasible budget allocations fairly and sustainably.

Worst-Case Higher Moment Coherent Risk Based on Optimal Transport with Application to Distributionally Robust Portfolio Optimization

The tail risk management is of great significance in the investment process. As an extension of the asymmetric tail risk measure—Conditional Value at Risk (CVaR), higher moment coherent risk (HMCR) is compatible with the higher moment information (skewness and kurtosis) of probability distribution of the asset returns as well as capturing distributional asymmetry. In order to overcome the difficulties arising from the asymmetry and ambiguity of the underlying distribution, we propose the Wasserstein distributionally robust mean-HMCR portfolio optimization model based on the kernel smoothing method and optimal transport, where the ambiguity set is defined as a Wasserstein “ball” around the empirical distribution in the weighted kernel density estimation (KDE) distribution function family. Leveraging Fenchel’s duality theory, we obtain the computationally tractable DCP (difference-of-convex programming) reformulations and show that the ambiguity version preserves the asymmetry of the HMCR measure. Primary empirical test results for portfolio selection demonstrate the efficiency of the proposed model.