GLASSO Estimation of Commodity Risks
In this paper we apply the Graphical LASSO (GLASSO) procedure to
estimate the network of twenty-four commodities divided in energy, agricultural
and metal sector. We follow a risk management perspective. We use GARCH and
Markov-Switching GARCH classes of models with different specifications for the
error terms, and we select those that best estimate Value-at-Risk for each commodity. We achieve GLASSO estimation exploring the precision matrix of the multivariate Gaussian distribution obtained from a Gaussian Copula, with marginals given