The correct understanding of commodity price dynamics can bring relevant improvements in terms of policy formulation both for developing and developed countries. Agricultural, metal and energy commodity prices might depend on each other: although we expect few important effects among the total...

In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the Focussed Information Criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different...

Modied Value at Risk (VaR) is an estimator of VaR based on the Cornish-Fisher expansion. It is fast to compute and reliable for non-normal returns. In this paper, we introduce modified Expected Shortfall as a new analytical estimator for Expected Shortfall (ES), another popular measure of...

Quadratic covariation is a popular descriptive measure for the volatility of a multivariate price process. It is consistently estimated by the sum of outer products of high-frequency returns.The proposed Realized Outlyingness Weighted Covariation (ROWCov) is a \emph{weighted} sum of outer...

Retailers use the Vector AutoRegressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available for not just one store, but a whole chain...

This paper compares the importance of different sovereign credit rating determinants over time, using a sample of 90 countries for the years 2002-2015. Applying the composite marginal likelihood approach, we estimate a multi-year ordered probit model for each of the three major credit rating...

We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings...

We develop a bivariate spectral Granger-causality test that can be applied at eachindividual frequency of the spectrum. The spectral approach to Granger causality hasthe distinct advantage that it allows to disentangle (potentially) di®erent Granger-causality relationships over di®erent time...