Enhancing Portfolio Allocation: A Random Matrix Theory Perspective

Fabio Vanni (), Asmerilda Hitaj and Elisa Mastrogiacomo
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Fabio Vanni: Department of Economics, University of Insubria, 21100 Varese, Italy
Asmerilda Hitaj: Department of Economics, University of Insubria, 21100 Varese, Italy
Elisa Mastrogiacomo: Department of Economics, University of Insubria, 21100 Varese, Italy

Mathematics, 2024, vol. 12, issue 9, 1-16

Abstract: This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, which are particularly susceptible to instabilities. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By comparing empirical correlations with those generated through RMT, we reveal non-random properties and uncover underlying relationships within financial data. We then utilize this methodology to construct the correlation network dependence structure used in portfolio optimization. The empirical analysis presented in this paper validates the effectiveness of RMT in enhancing portfolio diversification and risk management strategies. This research contributes by offering investors and portfolio managers with methodological insights to construct portfolios that are more stable, robust, and diversified. At the same time, it advances our comprehension of the intricate statistical principles underlying multivariate financial data.

Keywords: portfolio selection; networks; dependence structure; random matrix theory; Hilbert transformation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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