用garch-Copula
时间: 2023-11-21 11:04:06 浏览: 42
下面是使用GARCH-Copula方法计算VaR和ES值的示例代码:
```python
import pandas as pd
import numpy as np
from scipy.stats import norm, t
from arch import arch_model
from copulae import GaussianCopula
# 读取数据文件
data = pd.read_csv('data.csv', index_col=0)
returns = data.pct_change().dropna()
# 计算GARCH模型
garch_models = []
for i in range(len(returns.columns)):
garch_model = arch_model(returns.iloc[:, i], mean='Zero', vol='GARCH', p=1, q=1)
garch_result = garch_model.fit(disp='off')
garch_models.append(garch_result)
# 计算标准化残差
standardized_residuals = []
for i in range(len(returns.columns)):
standardized_residual = garch_models[i].resid / garch_models[i].conditional_volatility
standardized_residuals.append(pd.Series(standardized_residual))
# 计算Copula函数
copula = GaussianCopula.fit(pd.concat(standardized_residuals, axis=1).values)
# 计算VaR和ES值
confidence_level = 0.95 # 置信水平为95%
forecast_horizon = 1 # 预测1期后的收益率
forecasted_mean = np.zeros(len(returns.columns))
forecasted_std = np.zeros(len(returns.columns))
for i in range(len(returns.columns)):
# 预测未来收益率的均值和方差
garch_forecast = garch_models[i].forecast(horizon=forecast_horizon)
forecasted_mean[i] = garch_forecast.mean.iloc[-1]
forecasted_std[i] = np.sqrt(garch_forecast.variance.iloc[-1])
# 计算预测收益率的t值
t_values = norm.ppf(confidence_level, loc=forecasted_mean, scale=forecasted_std)
# 计算t-Copula的分位数
u_values = t.cdf(t_values, df=5)
quantiles = copula.ppf(u_values)
# 计算VaR和ES值
VaR = -np.percentile(quantiles.sum(axis=1), 100 - confidence_level * 100)
ES = -quantiles[quantiles.sum(axis=1) >= VaR].mean(axis=0)
print("VaR:", VaR)
print("ES:", ES)
```
在上述代码中,我们首先使用arch库中的arch_model函数计算了每只股票的GARCH模型,然后计算了标准化残差,并使用copulae库中的GaussianCopula函数计算了Gaussian Copula函数。接下来,我们计算了未来收益率的均值和方差,并通过t分布的累积分布函数计算了t值。然后,我们使用Copula函数计算了t-Copula的分位数,并计算了VaR和ES值。
需要注意的是,GARCH-Copula方法计算VaR和ES值需要考虑到多只股票之间的联动关系,因此需要进行更加复杂的计算。同时,该方法也需要进行更多的参数选择和模型检验,确保结果的可靠性和准确性。