python关于GARCH-MIDAS的代码

以下是一个使用Python实现GARCH-MIDAS模型的示例代码：

import yfinance as yf
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from arch import arch_model
from pandas_datareader import data as pdr
import datetime

# 设置起始和结束日期
start_date = datetime.datetime(2010, 1, 1)
end_date = datetime.datetime(2020, 12, 31)

# 从Yahoo Finance获取特斯拉股票数据
tsla = pdr.get_data_yahoo('TSLA', start=start_date, end=end_date)

# 计算每日收益率
tsla['returns'] = tsla['Adj Close'].pct_change()

# 计算每日波动率
tsla['volatility'] = np.log(tsla['Adj Close'] / tsla['Adj Close'].shift(1)).rolling(window=252).std() * math.sqrt(252)

# 计算MIDAS因子
tsla['midas_factor'] = tsla['Adj Close'].rolling(window=21).apply(lambda x: np.log(x[-1] / x[0]))

# 将数据拆分为训练集和测试集
train = tsla[:'2019']
test = tsla['2020':]

# 拟合GARCH-MIDAS模型
am = arch_model(train['returns'], x=train['midas_factor'], mean='Constant', vol='GARCH', p=1, o=1, q=1, dist='Normal')
res = am.fit(update_freq=5)

# 预测测试集中的波动率
forecasts = res.forecast(horizon=len(test), x=test['midas_factor'].values.reshape(-1, 1))

# 绘制预测结果
plt.plot(test['volatility'], label='True volatility')
plt.plot(forecasts.mean, label='Predicted volatility')
plt.legend()
plt.show()