We propose the use of copula methods to recover the dependence structure between prediction markets. Copula methods are flexible tools to measure associations among probabilities because they encompass both linear and non linear relationship among variables. We apply the proposed methodology to three actual prediction markets, the Saddam Security, the market of oil spot prices and the Saddamete...

We examine the dependence structure of electricity spot prices across regional markets in the Australian National Electricity Market (NEM). Our analysis is based on a GARCH approach to model the marginal price series in the considered regions in combination with copulae to capture the dependence structure between the different markets. We apply different copula models including Archimedean, ell...

This paper aims to propose a procedure for modeling the joint probability distribution of bivariate uncertain data with a nonlinear dependence structure. First, the concept of dependence measures is briefly introduced. Then, both the Akaike Information Criterion and the Bayesian Information Criterion are adopted for identifying the best-fit copula. Thereafter, simulation of copulas and bivariat...

Abstract. Tail order of copulas can be used to describe the strength of dependence in the tails of a joint distribution. When the value of tail order is larger than the dimension, it may lead to tail negative dependence. First, we prove results on conditions that lead to tail negative dependence for Archimedean copulas. Using the conditions, we construct new parametric copula families that poss...

For a multivariate vector X with discrete components, we construct, by means of explicit randomised transformations of X, multivariate couplings of copula representers U associated to X. As a result, we show that any copula can be constructed in this manner, giving a full probabilistic characterisation of the set of copula functions associated to X. The dependence properties of these copula rep...