给出21支股票日收益数据的garch-copula-var模型的python代码

以下是一个简单的21支股票日收益数据的GARCH-Copula-VaR模型的Python代码示例：

import pandas as pd
import numpy as np
import scipy.stats as st
import arch

# 导入数据
data = pd.read_csv('stock_returns.csv', index_col=0)

# 计算每支股票的收益率
returns = data.pct_change().dropna()

# 计算协方差矩阵和相关系数矩阵
cov_matrix = returns.cov()
corr_matrix = returns.corr()

# 估计每支股票的GARCH模型
garch_models = []
for i in range(len(returns.columns)):
    stock_return = returns.iloc[:, i]
    garch_model = arch.arch_model(stock_return, vol='GARCH', p=1, q=1)
    garch_models.append(garch_model.fit())

# 计算每支股票的残差
residuals = pd.DataFrame(index=returns.index, columns=returns.columns)
for i in range(len(returns.columns)):
    stock_return = returns.iloc[:, i]
    garch_model = garch_models[i]
    residuals.iloc[:, i] = garch_model.resid

# 拟合Copula模型
copula = arch.copula.GaussianCopula()
copula.fit(residuals)

# 估算VaR
alpha = 0.01
n_days = 1
portfolio_value = 1000000
icdf = st.norm.ppf(alpha)
simulated_returns = []
for i in range(n_days):
    # 生成标准正态残差
    u = copula.simulate(len(returns))
    z = st.norm.ppf(u)
    # 计算每支股票的条件方差
    cond_var = np.zeros(len(returns))
    for j in range(len(returns.columns)):
        garch_model = garch_models[j]
        cond_var += garch_model.conditional_volatility**2
    # 计算组合收益率和组合方差
    port_return = np.dot(np.sqrt(cond_var), z)
    port_var = np.dot(np.dot(z, cov_matrix), z)
    # 计算VaR
    simulated_returns.append(port_return)
    var = -portfolio_value * port_return - icdf * np.sqrt(portfolio_value * port_var)
    print('Day {}: VaR is {}'.format(i+1, var))

注意：本代码示例仅仅提供了一个简单的GARCH-Copula-VaR模型，实际应用中还需进行更加全面的数据分析、模型检验和风险控制。同时，代码中用到的收益率数据和模型参数均为示例数据，实际应用中需要进行相应的修改。