ECM-garch模型代码

以下是Python中实现ECM-GARCH模型的示例代码：

import numpy as np
import pandas as pd
from statsmodels.tsa.stattools import adfuller
from arch import arch_model

def ecm_garch(data, lag_order, vol_model='GARCH', dist='normal'):
    # 将数据处理为差分形式
    data_diff = np.diff(data)
    data_diff = np.insert(data_diff, 0, 0)

    # 计算阶数
    adf_result = adfuller(data_diff)
    if adf_result[0] > adf_result[4]['5%']:
        print('Error: time series is not stationary')
        return
    if lag_order is None:
        lag_order = int(np.log(len(data_diff)))

    # ECM模型
    lag_data = np.vstack([np.roll(data_diff, i+1) for i in range(lag_order)]).T
    lag_data = lag_data[lag_order:]
    data_diff = data_diff[lag_order:]
    b_hat = np.linalg.inv(lag_data.T @ lag_data) @ (lag_data.T @ data_diff)
    resid = data_diff - lag_data @ b_hat
    
    # GARCH模型
    vol = arch_model(resid, vol=vol_model, dist=dist)
    result = vol.fit(disp='off')

    return result.params, b_hat, result.conditional_volatility

# 使用示例
data = pd.read_csv('data.csv')['price'].values
params, b_hat, vol = ecm_garch(data, lag_order=1, vol_model='GARCH', dist='normal')
print('ECM-GARCH模型参数：', params)
print('ECM模型参数：', b_hat)
print('条件波动率：', vol)

注意，这里使用了Python中的statsmodels和arch库，需要先安装这两个库。此外，需要将数据处理为差分形式。