Simulating dependent normally distributed variables using copulas

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Alexandra
Alexandra on 10 Dec 2014
Answered: Tom Lane on 17 Dec 2014
I created a model that simulates variables with kernel distributions connected by copula functions. (X and Y are series of ln returns)
It looks like this: kX = ksdensity(X,X,'function','cdf'); kY = ksdensity(Y,Y,'function','cdf'); [Rho,nu] = copulafit('t',[kX kY],'Method','ApproximateML'); r = copularnd('t',Rho,nu,100000); XX = r(:,1); YY = r(:,1); SX = ksdensity(X,XX,'function','icdf'); SY = ksdensity(Y,YY,'function','icdf'); SimX = a*exp(SX); SimY = b*exp(SY);
Now I wish to evaluate the results when modeling the variables as normally distributed. I can’t see what function can directly substitute ksdensity in the model. I was trying to do this with:
kX = normcdf(X); kY = normcdf(Y); [Rho,nu] = copulafit('t',[kX kY],'Method','ApproximateML'); r = copularnd('t',Rho,nu,100000); XX = r(:,1); YY = r(:,1); SX = norminv(X,XX); SY = norminv(Y,YY); SimX = a*exp(SX); SimY = b*exp(SY);
But is not working because norminv does not support the second argument with the copula function. I am seeing this right? What can I do?
Thank you very much,

Answers (1)

Tom Lane
Tom Lane on 17 Dec 2014
Consider formatting your question so the code is not wrapped into the text.
The syntax ksdensity(x,xx) computes a kernel density based on x, evaluated at xx. If you intend to use the standard normal distribution, I believe you want just norminv(xx) .

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