GARCH-MIDAS code

Here is an example code for GARCH-MIDAS model in Python using the `arch` package:

import pandas as pd
import numpy as np
import arch

# Load data
data = pd.read_csv("financial_data.csv")

# Define MIDAS variables
y = data["y"] # Low-frequency variable
x = data["x"] # High-frequency variable

# Estimate MIDAS regression coefficients
midas_reg = arch.midas.MIDASRegressor()
midas_reg.fit(y, x)

# Compute MIDAS predicted values
y_pred = midas_reg.predict(x)

# Define GARCH-MIDAS model
garch_midas = arch.arch_model(y_pred, p=1, q=1, mean="AR", dist="Normal")

# Estimate GARCH-MIDAS parameters
garch_midas_fit = garch_midas.fit()

# Compute GARCH-MIDAS forecasts
garch_midas_forecast = garch_midas_fit.forecast(horizon=10)

# Print forecasts
print(garch_midas_forecast.mean.iloc[-1])

In the above code, we first load the financial data and define the high-frequency and low-frequency variables. We then use the `MIDASRegressor` class from the `arch` package to estimate the MIDAS regression coefficients and predict the low-frequency variable based on the high-frequency variable. We then define a GARCH-MIDAS model using the `arch_model` function and estimate the parameters of the model using the `fit` method. Finally, we use the `forecast` method to compute the GARCH-MIDAS forecasts for the next 10 periods and print the mean forecast for the last period. Again, please note that this is just an example code and you will need to adjust it to your specific needs and data.