E Pluribus, Quaedam: Gross Domestic Product out of a Dashboard of Indicators

Guerini, Marco; Vanni, Fabio; Napoletano, Mauro

doi:10.2139/ssrn.4150304

Cited by 1 publication

(1 citation statement)

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“…Following this, we describe the approach rooted in network theory (see [15]), offering a novel perspective on portfolio optimization. As also observed in recent studies [18,27] across various fields of economic literature, this methodological approach presents a modern and sophisticated way to addressing challenges in traditional portfolio selection processes. It aims to unveil an optimal solution by using the interconnected relationships among different assets.…”

Section: Portfolio Selection Modelsmentioning

confidence: 91%

Enhancing Portfolio Allocation: A Random Matrix Theory Perspective

Vanni,

Hitaj,

Mastrogiacomo

2024

Preprint

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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.

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Section: Portfolio Selection Modelsmentioning

confidence: 91%