What is copulas exactly, in probability and finance terms

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Discussion Overview

The discussion revolves around the concept of copulas in probability and finance, exploring their definition, components, and applications. Participants express confusion regarding the relationship between copulas, marginal distributions, and covariance matrices, as well as their use in modeling joint distributions.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses confusion about the definition of copulas, mentioning components such as uniform cumulative distribution functions (CDFs) and covariance matrices.
  • Another participant states that copulas represent joint probability distributions and highlight their ability to model correlation, while also noting the challenges in formulating different copulas.
  • There is a mention of Bayesian network modeling as an alternative to copulas, which can handle more variables but has its own downsides related to discretization.
  • A participant shares methods for simulating bivariate Gaussian copulas, including integration of density functions and using Cholesky decomposition with standard normal random variables.
  • References to R packages for copulas are provided, with discussions about accessing and reading the source code of these packages.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the definition and application of copulas, with multiple viewpoints and methods for simulation being discussed. The understanding of copulas and their components remains unclear for some participants.

Contextual Notes

Participants express uncertainty regarding the formulation of copulas and their relationship with marginal distributions and covariance matrices. There are also unresolved questions about the accessibility and understanding of R package source code.

Who May Find This Useful

This discussion may be useful for individuals interested in probability theory, finance, statistical modeling, and those looking to understand the practical applications of copulas and their computational implementation.

colstat
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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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