I've searched the internet for a R package that can support the model, but all the packages I find are only univariate, while I have several explanatory variables I want to include.

I've looked at the packages mfGARCH, GarchMidas, mcsGARCH, rumidas, rmgarch and midasr (I have attached the packages below), but it seems that none of them both support multiple variables while still estimating GARCH models. Is there something that I have overlooked, or have I simply misunderstand how to use the packages?

model_1 and model_2 are working properly. Model_3 is not working

```
df_midas_garch <- na.omit(df6[,c(2,3,9,11,12)])
model_1 <- fit_mfgarch(
data = df_midas_garch,
y = "sensex_return", # High-frequency data (daily returns)
x = "cr5", # Low-frequency data (HHI)
low.freq = "year_day", # Frequency of low-frequency data
K = 1 # Number of lags for the MIDAS filter
)
model_1
model_2 <- fit_mfgarch(
data = df_midas_garch,
y = "sensex_return",
x = "cr5",
low.freq = "year_day",
K = 1,
x.two = "GDP",
K.two = 1,
low.freq.two = "year_day"
)
model_2
model_3 <- fit_mfgarch(
data = df4,
y = "sensex_return",
x = "cr5",
low.freq = "year_day",
K = 1,
x.two = "GDP",
K.two = 1,
low.freq.two = "year_day",
x.three = "FCI",
K.three = 1,
low.freq.three = "year_day"
)
model_3
```

sensex_return is daily return on sensex, cr5 is concentration ratio is annual metric, GDP is also annual metric, FCI is also annual data.

Please help. Thanks in advance