Saturday, 14 February 2015

CopulaClass a Python class for using copulas: a fitting example

As I have already said in my previous post Copulalib is really user-friendly, it is difficult to write something easier, however I thought I might give it a try.

This class is built around Copulalib and since it is to be used with 2-dimensional copulas, it implements plots for data visualization and some other functions such as:
-showAvailableCopulas() a method to show visually what copulas are included in the package
-generateCopula() a method to generate the copula
-printCorrelation() this method prints out Spearman’s rho, Kendall’s tau and the fitted parameter
-getSimulatedData() this method retrieves your simulated data from the copula assuming your original data is normally distributed. It would be nice to implement some tool which could figure out the most likely distribution of your data and then use it to get the simulated observations. Perhaps in the future I’ll do it.

Furthermore, the class does not mind if you feed in python lists of numpy arrays as it turns x and y in numpy arrays. Be careful however that it does not check if your lists/arrays are of the same length.

Anyway, as for the result of the testing script below, the fitting of the Frank copula to the data seems to have been successful, our simulated data seems to fit the real quite nicely:


Originally our data was (very) approximately normally distributed, with some sort of positive correlation as you can clearly see from the plots below


Here below you can see 1000 simulated pseudo-observations from the Frank copula


Below you can find the code I used to generate this simple model:

And here are the correlation details

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