What is copulas exactly, in probability and finance terms

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SUMMARY

Copulas are joint probability distributions that model correlation between random variables, specifically through components such as uniform cumulative distribution function (CDF) marginals and a covariance matrix. The discussion highlights the use of bivariate Gaussian copulas and t-copulas, emphasizing their application in finance. The challenges of formulating various copulas are noted, alongside the advantages of Bayesian network modeling for handling multiple variables. Tools mentioned for simulating copulas include R packages and methods like Cholesky decomposition.

PREREQUISITES
  • Understanding of joint probability distributions
  • Familiarity with uniform cumulative distribution functions (CDFs)
  • Knowledge of covariance matrices
  • Basic proficiency in R programming and package management
NEXT STEPS
  • Explore the R package for copulas available at CRAN
  • Learn about Cholesky decomposition and its application in generating copulas
  • Research Bayesian networks and their advantages over traditional copula models
  • Read the article in Significance magazine regarding the applications of copulas in finance
USEFUL FOR

Statisticians, financial analysts, data scientists, and anyone interested in advanced modeling techniques for joint distributions and correlation analysis.

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Hi, all
I have been reading about copula, but still very confused.

What exactly is a copula? My understanding is: there are couple of components
1. uniform cdf marginal
2. a covariance matrix

What exactly is this thing? Why am I calculating the marginals and what does it have to do with the covariance matrix?

I am reading on the bivariate Gaussian copulas and t-copulas.
 
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Copula is just a joint probability distribution. The beauty is that copula models correlation. The downside is it is difficult to formulate different couplas. I like baysian network modelling of joint distributions. Baysian network handles more variables, not only bivariates; the downside is discretization. If you are reading the most recent Significance magazine you will find out all copula can do to the financing industry.
 
Thank you zlin034. What do you use to simulate copulas, Gaussian and student-t?

I have two ideas for bivariate Gaussian:
1. integrate the density from Wiki, here
http://en.wikipedia.org/wiki/Copula_(probability_theory)#Gaussian_copula

or this,
http://www.vosesoftware.com/ModelRi...n_ModelRisk/Copulas/Vose_Bivariate_Copula.htm

2. use Cholesky-decomposition \Sigma=A'A,
then, generate iid standard normal random variables V = (V1, V2)',
then, get Xi from A*V, for i=1,2.
then, get ui= \Phi(Xi), for i=1,2.
 
I am not using R, but even in R there's an algorithm right? Is there a way to see what they did in the package?
 
R is open source right? Please read the source code from the package
 
The compressed R packages have file extension .tar, they are called tar balls.

If you open the tar balls, you can see all sources codes are ASCII text files.
 

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