Question

我试图将代码从MATLAB转换为Python，但遇到一些错误。我的代码使用scipy.optimize包中的minimum函数来最小化对数似然函数。我也尝试过从同一包中提取fmin。由于受到限制，我收到与优化步骤相关的错误消息。变量“收益”是股票市场收益的向量。任何帮助将不胜感激。

MATLAB

function param = calibrate_GARCH(returns, startingValues)
% computes maximum likelhood estimates for a GARCH(1,1) process give
% timeseries "returns" using fminsearch function 
options = optimset('TolX',1e-12,'MaxFunEvals',3000,'Maxiter',3000,'Display', 
'off','LargeScale','off');
param = fminsearch(@(param)computeNegLLH(param,returns), startingValues, 
options);
end

% param for the given timeseries returns
sigma2 =  zeros(size(returns));

omega = param(1);
alpha = param(2);
beta  = param(3);
sigma2(1) = param(4);

% parameter restrictions: omega>0 alpha,beta>=0
% ensure that GARCH(1,1) process is covariance-stationary
if omega<=0 || min(alpha,beta)<0 || alpha + beta >= 1 || sigma2(1)<=0;
negLLH = intmax;
return;
end

% calculate squared returns
returns2 = returns.^2;

for t=2:length(returns)
sigma2(t)= omega + alpha*returns2(t-1) + beta*sigma2(t-1);
end
% computes (-1)xLogLikelihood for a GARCH(1,1) process with parameters
negLLH = 0.5*sum(log(sigma2) + returns2./sigma2);
end

Python 3

def computeNegLLH(param):
    sigma2 =  np.zeros(len(returns)); #zeros(size(returns));

    omega = param[0];
    alpha = param[1];
    beta  = param[2];
    sigma2[1] = param[3];

    # parameter restrictions: omega>0 alpha,beta>=0
    if (omega<=0 or min(alpha,beta)<0 or alpha + beta >= 1 or sigma2[0]<=0):
        negLLH = sys.maxsize;
        sys.exit();

    # calculate squared returns
    returns2 = returns**2;


   for t in range(1,len(returns)):
       sigma2[t]= omega + alpha*returns2[t-1] + beta*sigma2[t-1];

   # computes (-1)xLogLikelihood for a GARCH(1,1) process with parameters
   negLLH = 0.5*sum(log(sigma2) + returns2/sigma2);
   return negLLH


res = minimize(fun = computeNegLLH, x0 = startingValues, method = 'Nelder-Mead', tol = None, options = {'xtol': 1e-8, 'disp': True,'maxiter': 2000})

使用scipy包将GARCH（1,1）模型从MATLAB转换为Python

0 个答案: