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This poster describes the Target Dependence Coefficient.
This coefficient is designed to measure the dependence between random variables. Unlike other dependence measures which simply estimate the distance between the joint distribution and the distribution of independence (i.e. the product of marginals), this coefficient can target specific dependence relationships or forget others. To do so, dependence is measured as the relative distance from the forget-dependence (it can be independence) to the target-dependence (it can be comonotonicity, as usually considered). Earth Mover Distance (discrete optimal transport) is used to evaluate the distance between the different copulas encoding the data-dependence, target-dependence, forget-dependence. Finally, we use this methodology for clustering financial time series (5-year credit default swaps) and observe that it yields more stable clusters than using Pearson, Spearman or Kendall correlation.