Version 1
: Received: 10 April 2024 / Approved: 14 April 2024 / Online: 15 April 2024 (11:25:46 CEST)
How to cite:
Vanni, F.; Hitaj, A.; Mastrogiacomo, E. Enhancing Portfolio Allocation: A Random Matrix Theory Perspective. Preprints2024, 2024040872. https://doi.org/10.20944/preprints202404.0872.v1
Vanni, F.; Hitaj, A.; Mastrogiacomo, E. Enhancing Portfolio Allocation: A Random Matrix Theory Perspective. Preprints 2024, 2024040872. https://doi.org/10.20944/preprints202404.0872.v1
Vanni, F.; Hitaj, A.; Mastrogiacomo, E. Enhancing Portfolio Allocation: A Random Matrix Theory Perspective. Preprints2024, 2024040872. https://doi.org/10.20944/preprints202404.0872.v1
APA Style
Vanni, F., Hitaj, A., & Mastrogiacomo, E. (2024). Enhancing Portfolio Allocation: A Random Matrix Theory Perspective. Preprints. https://doi.org/10.20944/preprints202404.0872.v1
Chicago/Turabian Style
Vanni, F., Asmerilda Hitaj and Elisa Mastrogiacomo. 2024 "Enhancing Portfolio Allocation: A Random Matrix Theory Perspective" Preprints. https://doi.org/10.20944/preprints202404.0872.v1
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, particularly susceptible to instability.
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 contrasting empirical correlations with those derived from random matrices, we uncover non-random properties and 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
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
