A tail-revisited Markowitz mean-variance approach and a portfolio network centrality

Francesca Mariani (), Gloria Polinesi () and Maria Cristina Recchioni ()
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Francesca Mariani: Università Politecnica delle Marche
Gloria Polinesi: Università Politecnica delle Marche
Maria Cristina Recchioni: Università Politecnica delle Marche

Computational Management Science, 2022, vol. 19, issue 3, No 3, 425-455

Abstract: Abstract A measure for portfolio risk management is proposed by extending the Markowitz mean-variance approach to include the left-hand tail effects of asset returns. Two risk dimensions are captured: asset covariance risk along risk in left-hand tail similarity and volatility. The key ingredient is an informative set on the left-hand tail distributions of asset returns obtained by an adaptive clustering procedure. This set allows a left tail similarity and left tail volatility to be defined, thereby providing a definition for the left-tail-covariance-like matrix. The convex combination of the two covariance matrices generates a “two-dimensional” risk that, when applied to portfolio selection, provides a measure of its systemic vulnerability due to the asset centrality. This is done by simply associating a suitable node-weighted network with the portfolio. Higher values of this risk indicate an asset allocation suffering from too much exposure to volatile assets whose return dynamics behave too similarly in left-hand tail distributions and/or co-movements, as well as being too connected to each other. Minimizing these combined risks reduces losses and increases profits, with a low variability in the profit and loss distribution. The portfolio selection compares favorably with some competing approaches. An empirical analysis is made using exchange traded fund prices over the period January 2006–February 2018.

Keywords: Fintech; Adaptive clustering; Tail dependence; Financial time series; Asset allocation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10287-022-00422-2

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