A simulation of t-student copulas using Monte Carlo method

I am trying to do a simulation of t-student copulas using Monte Carlo method. However, I am not sure whether my way of doing it is right. Could you guys check whether I am right or wrong please?

Here is my code:

% do a Kernel smoothing function estimate for my five variables
U12=ksdensity(X1,X1,'function','cdf')
U22=ksdensity(X2,X2,'function','cdf')
U32=ksdensity(X3,X3,'function','cdf')
U42=ksdensity(X4,X4,'function','cdf')
U52=ksdensity(X5,X5,'function','cdf')

% calculate the estimate of the correlation matrix and number of degrees
[rho_N_t,nuhat,nuci]=copulafit('t',[ U12(:),U22(:),U32(:),U42(:),U52(:)])

%  Kendall's rank correlation
Kendall_t = copulastat('t',rho_N_t)

% Find the Cholesky decomposition
L = chol(Kendall_t,'lower')

U = zeros(10000,5);
% repeat 10000 times
for n=1:10000
    % Simulate 5  independent random variates from N(0,1).
    z = normrnd(0,1,5,1)
    y = L*z
    % Simulate a random variate s from Chi-square distribution 
    s = chi2rnd(nuhat)
    x = sqrt(v)/sqrt(s)*y
    u = tcdf(x,v)
    u_c = u'
    U(n,:) = u_c
end

% return to the original scale 
yy1= ksdensity(X1, U(:,1),'function','icdf')
yy2= ksdensity(X2, U(:,2),'function','icdf')
yy3= ksdensity(X3, U(:,3),'function','icdf')
yy4= ksdensity(X4, U(:,4),'function','icdf')
yy5= ksdensity(X5, U(:,5),'function','icdf')