# ARMA-GARCH estimation with EGB2 distribution

I want to estimate a ARMA-GARCH model by using the EGB2 distribution instead of the normal distribution. The model I want to estimate is: $$y_t = \mu + \phi_1 y_{t-6} + \phi_2 y_{t-8} + \theta_1 \epsilon_{t-1} + \epsilon_t$$ $$\sigma^2_t = \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2$$ The loglikelihood for the EGB2 is: $$\text{log L} = T[log(\sqrt{\Omega} - log(B(p,q)) + p\Delta] + \sum \Bigg[p\Bigg(\frac{\sqrt{\Omega}\epsilon_t}{\sqrt{\sigma_t^2}}\Bigg)\\ - 0.5 log(\sigma^2_t) - (p+q)log\Bigg(1+exp\frac{\sqrt{\Omega}\epsilon_t}{\sqrt{\sigma_t^2}} + \Delta\Bigg) \Bigg].$$

Now, I am going to minimize this function:

loglike = 0;
for i = 1:length(R)-8
loglike = loglike + (p*sqrt(OMEGA)*eps(i)/sqrt(sigma_2(i)) - 0.5*log(sigma_2(i)) - (p+q)*log(1+exp(sqrt(OMEGA)*eps(i)/sqrt(sigma_2(i))) + DELTA));
end

logL = -(length(eps)*(log(sqrt(OMEGA))-log(betaFunc(p,q))+p*DELTA) + loglike);


And the constraints where that $$\omega, \alpha, \beta, p, q>0$$ and that $$\alpha+ \beta <1$$. The answer is that I must get a loglikelihood value around -3000. But I get 2.5+e07. What is going wrong? Must I have more restrictions?

I really hope someone can help me Thanks in advance