Tuning Univariate Garch Models"

  collapse = TRUE,
  comment = "#>",

  fig.align = "center",
  fig.width = 7,
  fig.height = 5,

  message = FALSE,
  warning = FALSE

This short tutorial teaches you how to tune parameters of univariate garch models. The function used for this type of models is the garch_reg() function and the parameters that you can tune are the following:


First, we load the packages:


We will use the rIBM dataset which is a tibble containing a date column and another column with the daily returns for the IBM company from 2007 to 2020. Extenderemos este dataset en el horizonte temporal tres fechas We will extend this dataset over the forecast horizon three dates and create our training and future dataset.

rIBM_extended <- rIBM %>%
    future_frame(.length_out = 3, .bind_data = TRUE) 

rIBM_train  <- rIBM_extended %>% drop_na()
rIBM_future <- rIBM_extended %>% filter(is.na(daily_returns))

Next, we define the model specification and mark which parameters we are going to tune. It is also necessary to use the tune_by argument in which we must specify either sigmaFor or seriesFor to refer to the series forecasts or sigma forecasts (based on these forecasts will be used in the tuning process and the metrics will be calculated).

# Model Spec
model_spec <-garchmodels::garch_reg(mode = "regression",
                                    arch_order = tune(),
                                    garch_order = tune(),
                                    tune_by = "sigmaFor") %>%

The next step is to create a recipe in which we specify the formula we will use:

# Recipe spec
recipe_spec <- recipe(daily_returns ~ date, data = rIBM_train) 

We put everything together in a workflow:

# Workflow
wflw <- workflow() %>%
    add_recipe(recipe_spec) %>%

Next, we need to generate the resamples. As it is a time series problem, the order is important in our observations so we will use the timetk function and visualize:

resamples <- timetk::time_series_cv(rIBM_train, 
                                    date_var = date, 
                                    initial = "6 years", 
                                    assess = "24 months",
                                    skip = "24 months",
                                    cumulative = TRUE,
                                    slice_limit = 3)

timetk::plot_time_series_cv_plan(resamples, .date_var = date, .value = daily_returns)

Finally, we use the tune_grid() function to apply the workflow on the resamples in a parallel way.

tune_results <- tune_grid(
    object     = wflw,
    resamples  = resamples,
    param_info = parameters(wflw),
    grid       = 5,
    control    = control_grid(verbose = TRUE, allow_par = TRUE, parallel_over = "everything")

Finally, we can see which are the best results ordered by RMSE:

tune_results %>% show_best(metric = "rmse")

Try the garchmodels package in your browser

Any scripts or data that you put into this service are public.

garchmodels documentation built on April 13, 2021, 1:05 a.m.