You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: diagonal_bekk_T_est_likelihood.m
    path of file: ./repos/malini/Codes/FC_EC_calculation code/DynamicFC/jplv7/Ucsd_garch/MV Garch/T Bekk
    
    the code of the file until where you have to start completion: function [LLF,likelihoods,Ht]=diagonal_bekk_likelihood(parameters,errors,p,q,k,k2,t);
% PURPOSE:
%      To Estimate a diagonal BEKK multivariate GARCH likelihood.
% 
% 
% USAGE:
%      [LLF,likelihoods,Ht]=diagonal_bekk_mvgarch_likelihood(parameters,errors,p,q,k,k2,t);
% 
% 
% INPUTS:
%      parameters - a k*(k+1)/2 + k*p +k*q vector of model parameters of the form
%                   [ivech(C);diag(A(1));...;diag(A(p));diag(B,); ...diag(B(q))]
%      errors     - A zeromean t by k martix of residuals
%      p          - The lag length of the innovation process
%      q          - The lag length of the AR process
%      k          - The number of data series
%      k2         - k*k

    
function [LLF,likelihoods,Ht]=diagonal_bekk_likelihood(parameters,errors,p,q,k,k2,t);
% PURPOSE:
%      To Estimate a diagonal BEKK multivariate GARCH likelihood.
% 
% 
% USAGE:
%      [LLF,likelihoods,Ht]=diagonal_bekk_mvgarch_likelihood(parameters,errors,p,q,k,k2,t);
% 
% 
% INPUTS:
%      parameters - a k*(k+1)/2 + k*p +k*q vector of model parameters of the form
%                   [ivech(C);diag(A(1));...;diag(A(p));diag(B,); ...diag(B(q))]
%      errors     - A zeromean t by k martix of residuals
%      p          - The lag length of the innovation process
%      q          - The lag length of the AR process
%      k          - The number of data series
%      k2         - k*k
%      t          - the length of the data series
% 
% OUTPUTS:
%      LLF           - The loglikelihood of the function at the optimum
%      Ht            - A k x k x t 3 dimension matrix of conditional covariances
%      likelihoods   - A t by 1 vector of individual likelihoods
% 
% COMMENTS:
% 
% 
% Author: Kevin Sheppard
% kevin.sheppard@economics.ox.ac.uk
% Revision: 2    Date: 12/31/2001
 


%The first k(k+1)/2 parameters are C, the next p are A, and the next q are B
C=parameters(1:(k2));
A=parameters(k2+1:k2+k*p);
B=parameters(k2+k*p+1:k2+k*p+k*q);
nu=parameters(k2+k*p+k*q+1)^2+2.1;
tempA=zeros(k,k,p);
tempB=zeros(k,k,p);
for i=1:p
    tempA(:,:,i)=diag(A((k*(i-1)+1):(k*i)));
end