[Winbugs] Fail: Mixed matrices with stoch./ det. components

#1
Dear all,
I stumpled upon some really good answers in this forum so I decided to give it a try and hope that I can also enrich the ongoing discussions here.

I try to implement a multivariate stochastic volatility model in Winbugs. Hereby I use the matrix Sigma.epsilon with stochastic parts in the off-diagonal elements (correlations) and deterministic values in the diagonals.

However, when investigating the draws of Sigma.epsilon after sampling, Winbugs returns only matrices with 0's in the diagonal.
I do not see where the mistake could be, every comment is appreciated
Code:
   model{ 
    

    for (t in 1:T) 
    {

        Omega[t,1,1]    <- exp(h[1,t]); 
        Omega[t,2,2]    <- exp(h[2,t]);
        Omega[t,1,2]    <- 0; 
        Omega[t,2,1]    <- 0;

       Sigma.epsilon[t,1,1] <- 1;
       Sigma.epsilon[t,2,2] <- 1;
       Sigma.epsilon[t,1,2] <- rho.e;
       Sigma.epsilon[t,2,1] <- rho.e;
      
        for (n in 1:N){
            for (j in 1:N){
            Var.y[t,n,j]      <- Omega[t,n,j]*Sigma.epsilon[t,n,j]*Omega[t,j,n];
        }}        
        Prec.y[t,1:N,1:N]<-inverse(Var.y[t,,])
        y[t,1:2]        ~  dmnorm(mu.y[1:2],Prec.y[t,,]);      
    }

    for (n in 1:N) 
    { 
        h.st[1,n] <- mu.h[n]
        for (t in 2:T) 
        { 
            h.st[t,n] ~dnorm(mu.h[n],1);
            h.mu[t,n] <- mu.h[n] + ph[n]*(h.st[t-1,n]-mu.h[n]);
        }
    }

    for (t in 1:T) 
    {
        h[1,t] <- sig.u[1]*h.st[t,1];
        h[2,t] <- sig.u[2]*rho.u*h.st[t,1]+sig.u[2]*sqrt(1-rho.u*rho.u)*h.st[t,2];
    }

    # priors
    for (n in 1:N) 
    {
        invsig2u[n] ~dgamma(2.5,0.025); 
        sig.u[n] <- sqrt(1/invsig2u[n]);
        phstar[n] ~dbeta(20,1.5); 
        ph[n] <- 2*phstar[n] -1; 
        mu.h[n] ~dnorm(0,0.04);
    }

    rho.e ~dunif(-1,1); 
    rho.u ~dunif(0,1);
    mu.y[1] <- 0;
    mu.y[2] <- 0;
}