This article presents an investigation of agricultural production risk over time and across space and its implications for food security. The econometric approach involves a Quantile Autoregressive (QAR) model and a copula to provide a flexible representation of the distribution of yield risk and its evolution over time and across space. The analysis relies on a two‐step estimation method to evaluate the multivariate yield distribution and its spatial and temporal evolution. Linkages between agricultural production risk and the economics of food security are explored, with implications for the welfare cost of food insecurity. The approach is illustrated in an econometric application to regional wheat and corn yields in Italy. The analysis provides new and useful information on the evolving linkages between agricultural production risk, productivity, and food security. Our integrated approach documents the role of regional diversification and of productivity growth along with their effects on food security.

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In this paper some Archimedean copula functions for bivariate financial returns are studied. The choice of\ud this family is due to their ability to capture the tail dependence, which is an association measurewecan detect\ud in many bivariate financial time-series.Atime-varying version of these copulae is also investigated. Finally,\ud the Value-at-Risk is computed and its performance is compared across different copula specifications

In this paper we propose a heuristic strategy aimed at selecting and analysing a set of financial assets, focusing attention on their multivariate tail dependence structure. The selection, obtained through an algorithmic procedure based on data mining tools, assumes the existence of a reference asset we are specifically interested to. The procedure allows one to opt for two alternatives: to prefer those assets exhibiting either a minimum lower tail dependence or a maximum upper tail dependence. The former could be a recommendable opportunity in a financial crisis period. For the selected assets, the tail dependence coefficients are estimated by means of a proper multivariate copula function. Copyright © 2010 John Wiley & Sons, Ltd.

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