Graphical Models for Commodities: A Quantile Approach

Beatrice Foroni (), Luca Merlo and Lea Petrella
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Beatrice Foroni: Sapienza University of Rome, MEMOTEF Department
Luca Merlo: Sapienza University of Rome, Department of Statistical Sciences
Lea Petrella: Sapienza University of Rome, MEMOTEF Department

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 253-259 from Springer

Abstract: Abstract The high level of integration of international financial markets highlights the need to accurately assess contagion and systemic risk under different market conditions. To this end, we develop a quantile graphical model to identify the tail conditional dependence structure in multivariate data across different quantiles of the marginal distributions of the variables of interest. To implement the procedure, we consider the Multivariate Asymmetric Laplace distribution and exploit its location-scale mixture representation to build a penalized EM algorithm for estimating the sparse precision matrix of the distribution by means of an $$L_1$$ L 1 penalty. The empirical application is performed on a large set of commodities representative of the energy, agricultural and metal sectors.

Keywords: EM algorithm; Energy commodities; Graphical models; Multivariate asymmetric laplace distribution (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_41

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DOI: 10.1007/978-3-030-99638-3_41

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