  The notion of a zenpath and a zenplot is introduced to search and detect
dependence in high-dimensional data for model building and statistical
inference. By using any measure of dependence between two random variables
(such as correlation, Spearman's rho, Kendall's tau, tail dependence etc.), a
zenpath can construct paths through pairs of variables in different ways, which
can then be laid out and displayed by a zenplot. The approach is illustrated by
investigating tail dependence and model fit in constituent data of the S&P 500
during the financial crisis of 2007-2008. The corresponding Global Industry
Classification Standard (GICS) sector information is also addressed.
  Zenpaths and zenplots are useful tools for exploring dependence in
high-dimensional data, for example, from the realm of finance, insurance and
quantitative risk management. All presented algorithms are implemented using
the R package zenplots and all examples and graphics in the paper can be
reproduced using the accompanying demo SP500.
