Tools and Parameter Tuning for Time-Varying BBX Copulas

Summary

The document responds to a request for time-varying BBX copula implementations in Matlab or R. It points to R packages that provide BBX families and spatial or spatiotemporal bivariate copulas, while noting that temporal variation may require additional implementation because it was not part of the cited package’s original design.

For parameter tuning, it suggests linking distance to a dependence measure such as Kendall’s tau or Spearman’s rho. Tail dependence coefficients can also be used as a tuning target, with numerical inversion as a possible starting point. The response cautions that inversion may yield multiple parameter solutions, so tail dependence alone may not identify a sensible fit; additional criteria may be needed. It offers implementation leads and fitting ideas rather than a complete time-varying procedure or comparative evidence about model performance.

Key ideas

• R packages offer BBX copula families and spatially varying bivariate copula functionality.
• Temporal variation may need to be added beyond the packages’ initial design.
• A dependence function can connect distance with Kendall’s tau or Spearman’s rho.
• Tail dependence coefficients can help tune parameters, but numerical inversion may not give a unique fit.

Tags

# Where can I find implementations of the time-varying copula (BBX) in Matlab or R?


# Where can I find implementations of the time-varying copula (BBX) in Matlab or R?












I want to construct some time-varying BBX copulas, however, I found that patron's package does not contain time-varying BBX copula.

Anybody know where I can download them?

## Answer by Ben (score 2, accepted)

https://quant.stackexchange.com/a/11202

Implementations of the BBx families are available from the VineCopula R-package from CRAN. Spatially and spatio-temporally varying bivariate copulas are provided through the R-package spcopula from r-forge. Temporal support will need some additional work as it was not part of the initial design. The tuning of the copulas' parameter can be done via a dependence function that relates distance with Kendall's tau or Spearman's rho.

You will find the method "tailIndex" (from package copula) that provides the upper and lower tail coefficients for any copula object passed to it. Numerical inversion could be a first attempt to use it as parameter tuning function. However, be aware that the solution might not be unique (i.e. for two or more parameter families). Additional criteria will most likely be necessary for sensible fits.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

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