Ricerc@Sapienza

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

In this paper we investigate different VaR forecasts for daily energy commodities returns using GARCH, EGARCH, GJR-GARCH, Generalized Autoregressive Score (GAS) and the Conditional Autoregressive Value at Risk (CAViaR) models. We further develop a Dynamic Quantile Regression (DQR) one where the parameters evolve over time following a first order stochastic process. The models considered are selected employing the Model Confidence Set procedure of Hansen et al. (2011) which provides a superior set of models by testing the null hypothesis of Equal Predictive Ability.

Accurate Value at Risk measurement often requires estimation of complex dynamic models where usually the parameters enter nonlinearly the quantile estimation equation. IN this paper we address the problem of estimation of the parameters of a class of conditionally autoregressive Value at Risk models by adapting the Majorizing-Minorizing algorithm of Hunter and Lange (2000)

An accurate assessment of tail dependencies of financial returns is key for risk management and portfolio allocation. In this paper we consider a multiple linear quantile regression setting for joint prediction of tail risk measures, namely Value at Risk(Var) and Expected Shortfall (ES) using a generalization of the Multivariare Asymmetric Laplace distribution. The proposed method permits simultaneous modelling of multiple conditional quantiles of a multivariate response variable and allows to study the dependence structure among financial assets at different quantile levels.