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A Network-Based Approach to the Implications of the COVID-19 Pandemic on the Latin American Minimum Variance Portfolio
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Author(s): Lucas Pagel de Souza (Hamburg University of Applied Sciences, Germany), Natallia Katenka (The University of Rhode Island, USA)and Natalia Ribberink (Hamburg University of Applied Sciences, Germany)
Copyright: 2025
Pages: 34
Source title:
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
Source Author(s)/Editor(s): Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Jimbo Henri Claver (Samarkand Interntional University of Technology, Uzbekistan), A. V. Sriharsha (Mohan Babu University, India), Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania)and Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)
DOI: 10.4018/979-8-3693-6215-0.ch013
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Abstract
Since the COVID-19 pandemic outbreak a growing body of literature has observed uncertainty among the main factors driving investor behavior, with these becoming more likely to seek risk minimization strategies, such as the minimum variance approach based on Modern Portfolio Theory. In this chapter a network-based minimum variance approach to portfolio diversification is carried out with assets from Latin America's five largest stock exchanges between 2018 and 2021. Global Minimum Variance Portfolios are obtained with the use of structural information contained in partial-correlation networks and a community detection process. The main objective is the investigation of the implications of the pandemic on minimum variance diversification in the region through an analysis of portfolio asset selection and allocation results, including risk and return performance, benchmarked against naïve investment portfolios.
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