Portfolio Analytics via Dynamic Graph Learning: Modelling and Testing

doi:10.1016/j.jfranklin.2026.108655

Portfolio Analytics via Dynamic Graph Learning: Modelling and Testing

2026-04-16

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Ragnar Gudmundarson *

Dimitris Christopoulos

DOI：10.1016/j.jfranklin.2026.108655

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The literature on dynamic graphical models has mainly focused on Gaussian dependence structures. This paper extends those models to non-Gaussian settings by introducing a framework based on the skew group-t family of distributions. This family includes the Student-t, skew-t, and grouped skew-t copula models as special cases.

Keywords:

Dynamic graphical models

Non-Gaussian dependence

Skew group-t family

Portfolio analytics

Graph learning

Portfolio Analytics via Dynamic Graph Learning: Modelling and Testing

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