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How can I simulate data from two different distribution using Copulas?

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Punto on 5 Dec 2019
Commented: Punto on 6 Dec 2019
I have been trying to create a Monte Carlo simulation model based on two set of data. Data X seem to fit a gamma distribution and data Y seem to fit either gumbel or lognormal distribution (lets assume it is lognormal for the sake of it).
So I basically tried to follow the guide "Simulating dependent random variables using copulas" but I can't seem to make it work. Probably because I don't fully get the whole concept behind it, but I know it should work for what I want to get. Right now I just seem to generate random values from 0 to 1 but I need it to do this in correlation to my data.
This is my code:
Z = mvnrnd([0 0],[1 rho;rho 1], n);
U = normcdf(Z);
Q = [gaminv(U(:,1),2,1) exp(U(:,2))];
So what is left is to insert my data to the simulation, and I also assume I would have to fit my data using the gamfit and lognfit function.


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Accepted Answer

Jeff Miller
Jeff Miller on 6 Dec 2019
OK, then I think you are almost there. You have the data X, Y, and rhoXY. Start by estimating the parameters for your gamma and lognormal distributions, like this:
Xparms = gamfit(X);
Yparms = lognfit(Y);
Next you need to adjust a parameter that I'll call rhoZ. Run the following section repeatedly, changing the value of rhoZ each time until the final GivesCorr value matches (closely enough) the observed rhoXY in your real data.
BIGN = 1000000;
rhoZ = 0.35; % Change the 0.35 until GivesCorr (below) matches your observed rhoXY
Z = mvnrnd([0 0],[1 rhoZ;rhoZ 1], BIGN);
U = normcdf(Z);
Xrnd = gaminv(U(:,1),Xparms(1),Xparms(2));
Yrnd = logninv(U(:,2),Yparms(1),Yparms(2));
GivesCorr = corr(Xrnd,Yrnd)
Now, using the rhoZ that you identified above, you can generate your X,Y random samples with the n you really want:
n = 1000;
Z = mvnrnd([0 0],[1 rhoZ;rhoZ 1], n);
U = normcdf(Z);
Xrnd = gaminv(U(:,1),Xparms(1),Xparms(2));
Yrnd = logninv(U(:,2),Yparms(1),Yparms(2));
I think the Xrnd and Yrnd that will result from this process will be what you want.

More Answers (1)

Jeff Miller
Jeff Miller on 5 Dec 2019
The question is a little confusing because the U values are from 0 to 1 but the Q values aren't.
Q(1,:) looks like a gamma but Q(2,:) doesn't look like either gumbel or lognormal.
Maybe you want :
Q = [gaminv(U(:,1),2,1) exp(Z(:,2))]; % to get variable 2 as a lognormal


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Punto on 6 Dec 2019
Sorry for being unclear. The porpuse is to write a program for a monte carlo simulation while also considering the correlation between data X and Y.
So with this program I want to compute results from my data against random inputs I generate in mvrnd.
From the example I refered to, I kinda gathered I have to acount for the correlation my including the rho value, as thats how they did it. I am probably misunderstanding something essential in that example I am reffering, which makes it more confusing.
Jeff Miller
Jeff Miller on 6 Dec 2019
Writing a program for monte carlo simulation could mean 100 different are not specifying any of the details, and these matter.
Let me try again: See my question about (1) - (3) above: Is this a fair summary of your situation. Please just answer yes if it is, and explain why if it is not.
Punto on 6 Dec 2019
1. Yes, however mistakenly I wrote gumbel but meant gamma, sorry about that!
2. Yes
3. Yes

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