Purpose: The purpose of this research is to examine the application of portfolio optimization in the context of real-world financial issues, particularly in light of the challenges posed by the COVID-19 pandemic. Traditional portfolio optimization strategies, such as those proposed by Markowitz, often rely on the assumption of normally distributed returns, which may not accurately capture the risks associated with extreme events like the COVID-19 crisis. This study aims to shed new light on portfolio optimization methods by exploring various approaches and considering the implications of non-normally distributed returns on portfolio construction.
Methods: This research employs a quantitative approach to analyze portfolio optimization techniques in the face of non-normally distributed returns. Using data from financial markets impacted by the COVID-19 pandemic, the study investigates different portfolio construction methods, including risk-free rate for equally weighted portfolios, optimal risk portfolios, minimum variance weights, and maximum expected returns. Various risk metrics such as variance, Sharpe ratio, and standard deviation are considered to evaluate portfolio performance under different constraints.
Results and discussion: The empirical findings highlight the limitations of traditional portfolio optimization techniques, particularly in accurately assessing and managing risk in the presence of non-normally distributed returns. Assets exhibit heavily tailed returns, leading to an underestimation of risk when using standard approaches. The study identifies that certain assets offer high returns but also entail significant risks, necessitating a nuanced approach to portfolio construction. By considering stable distribution models and optimizing for both maximum expected return and minimum variance weight, investors can build more profitable and diversified portfolios while managing risk effectively.
Implications of the research: The research findings have important implications for investors and financial practitioners, particularly in navigating uncertain market conditions such as those brought about by the COVID-19 pandemic. By recognizing the limitations of traditional portfolio optimization methods and embracing more sophisticated approaches that account for non-normally distributed returns, investors can make more informed decisions and better manage portfolio risk. These insights can inform the development of robust investment strategies tailored to mitigate the impact of extreme events on portfolio performance.
Originality/value: This research contributes to the literature by offering a fresh perspective on portfolio optimization under the backdrop of the COVID-19 pandemic. By systematically evaluating different portfolio construction methods and considering the implications of non-normally distributed returns, the study advances understanding in the field of financial risk management. The identification of stable distribution models and the emphasis on balancing maximum expected return with minimum variance weight provide practical guidance for investors seeking to build resilient and profitable portfolios in turbulent market environments. Overall, this research underscores the importance of adapting portfolio optimization strategies to address the realities of contemporary financial markets.
