Vine Copula Partner Selection Using Dependence and Tail Measures

Summary

This code provides helper measures for selecting groups of four stocks as candidate partners in a vine copula workflow. One traditional method chooses the quadruple with the greatest sum of pairwise correlations. Other methods operate on empirical ranked returns: an average of three multivariate extensions of Spearman's rho, and a geometric score that favors observations close to the multidimensional diagonal. A further function computes a chi-squared statistic for a four-dimensional Nelsen copula using a covariance matrix obtained by numerical integration.

The functions return a selected quadruple and its score, or supporting sector metadata and ranked quantiles. They describe candidate-selection calculations, not a complete trading strategy or a tested investment result. The snippet assumes data and combinations have already been prepared, and its methods depend on the chosen dependence measure; it gives no validation of selection stability, out-of-sample performance, transaction costs, or suitability for portfolio construction.

Key ideas

• The utilities rank four-stock groups using correlation, multivariate rank dependence, or geometric proximity to a diagonal.
• Empirical cumulative distributions convert returns to ranked quantiles for dependence calculations.
• An extremal-dependence function evaluates a four-dimensional copula statistic using a numerically integrated covariance matrix.
• The code supports partner selection but does not provide a trading strategy or evidence of investment performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.