Rotationally invariant estimators on portfolio optimization to unveil financial risk’s states

Andrés García Medina and Rodrigo Macías Páez
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Andrés García Medina: Consejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor 03940, Ciudad de México, México2Centro de Investigación en Matemáticas, Unidad Monterrey, Av. Alianza Centro 502, PIIT 66628, Apodaca, Nuevo León, México
Rodrigo Macías Páez: Centro de Investigación en Matemáticas, Unidad Monterrey, Av. Alianza Centro 502, PIIT 66628, Apodaca, Nuevo León, México

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 09, 1-19

Abstract: Rotationally Invariant Estimators (RIE) are a new family of covariance matrix estimators based on random matrix theory and free probability. The family RIE has been proposed to improve the performance of an investment portfolio in the Markowitz model’s framework. Here, we apply state-of-the-art RIE techniques to improve the estimation of financial states via the correlation matrix. The Synthesized Clustering (SYNCLUS) and a dynamic programming algorithm for optimal one-dimensional clustering were employed to that aim. We found that the RIE estimations of the minimum portfolio risk increase the Active Information Storage (AIS) in the American and European markets. AIS’s local dynamic also mimics financial states’ behavior when estimating under the one-dimensional clustering algorithm. Our results suggest that in times of financial turbulence, RIE estimates can be of great advantage in minimizing risk exposure.

Keywords: Random matrix theory; RIE; clustering analysis; financial risk (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123501176

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