The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

GINN — GARCH-Informed Neural Network

An R package implementing the GARCH-Informed Neural Network (GINN) hybrid framework for volatility (variance) forecasting.

The GINN Pipeline (matches the paper exactly)

Step 1: y_t  →  r_t = (y_t - y_{t-1}) / y_{t-1}        [returns]
Step 2: AR(p) on r_t  →  μ̂_t                            [mean return]
Step 3: GARCH(p,q) on r_t  →  σ²̂_GARCH                 [conditional variance]
Step 4: σ²_t = (r_t - μ̂_t)²                             [ground-truth variance]
Step 5: LSTM trained on σ²_t sequences with GINN loss:
        Loss = λ × MSE(σ²_t, σ²̂_LSTM)                  [vs ground truth]
             + (1-λ) × MSE(σ²̂_GARCH, σ²̂_LSTM)          [vs GARCH  — Eq. 17]

Lambda interpretation: - λ = 1.0 → Standard LSTM (ground-truth variance only, no GARCH guidance) - λ = 0.5 → Balanced GINN (equal weight) - λ = 0.0 → GINN-0 (LSTM only learns from GARCH)

Installation

install.packages(c("torch", "rugarch", "ggplot2", "cli", "coro", "devtools"))
torch::install_torch()         # one-time ~500 MB download
devtools::install("path/to/GINN")

Quick Start

library(GINN)

# Simulated prices
prices <- cumprod(1 + rnorm(150, 0.001, 0.02)) * 100

# ── AUTO mode ─────────────────────────────────────────────────
# AR lag, GARCH config, LSTM hyperparameters all auto-selected
result <- GINN(
  data        = prices,
  mode        = "auto",
  seq_len     = 5,
  lambda_list = seq(0.1, 0.9, 0.1)
)
print(result)
plotGINN(result, h = 12)
predictGINN(result, h = 12)
plotlambda(result)
accuracy.GINN(result)   # this one stays   # full metrics table

# ── MANUAL mode ───────────────────────────────────────────────
result_m <- GINN(
  data       = prices,
  mode       = "manual",
  ar_lag     = 1,
  garch_p    = 1, garch_q = 1,
  garch_mean = "zero", garch_dist = "norm",
  seq_len    = 5, hidden_size = 32,
  num_layers = 1, lr = 0.001, dropout = 0.0,
  lambda_list = c(0.1, 0.5, 0.9)
)
print(result_m)

Parameters

Parameter Default Description
data required Numeric price/yield vector
mode "auto" "auto" or "manual"
ar_max_lag 5 Max AR lag to search (auto)
ar_lag NULL Fixed AR lag (manual)
garch_p_grid 1:3 ARCH order grid (auto)
garch_q_grid 1:3 GARCH order grid (auto)
garch_mean_grid c("zero","arma") Mean model grid (auto)
garch_dist_grid c("norm","std") Distribution grid (auto)
garch_p/q/mean/dist NULL Fixed GARCH config (manual)
seq_len 5 Variance sequence length
hidden_size 32 LSTM hidden units (manual)
num_layers 1 Stacked layers (manual)
lr 0.001 Learning rate (manual)
dropout 0.0 Dropout rate (manual)
epochs 500 Max training epochs
patience 30 Early-stopping patience
tune_epochs 50 Epochs per grid combo (auto)
batch_size 16 Mini-batch size
lambda_list 0.1…0.9 Lambda values to test
hidden_grid c(8,16,24,32,40) Hidden grid (auto)
layers_grid c(1,2) Layers grid (auto)
lr_grid c(0.001,0.003,0.005) LR grid (auto)
dropout_grid c(0.0,0.1,0.2,0.3) Dropout grid (auto)

Output Object (GINN class)

result$results       # list — one per lambda (RMSE, MAE, R², preds, loss_hist)
result$garch         # GARCH config, coefs, sigma²_train/test, metrics
result$ar            # AR lag, fitted values, forecast
result$returns       # computed return series r_t
result$variance      # ground-truth variance σ²_t (train/test/all)
result$best_model    # complete best model summary
result$best_hp       # best LSTM hyperparameters
result$tuning_log    # grid-search log (auto mode)
result$meta          # settings
result$data_info     # original data, split, N

Methods

print(result)                    # compact table (all lambdas)
summary(result)                  # full data frame with all metrics
plotGINN(result, h = 12)              # actual + predicted + forecast
plotGINN(result, h = 12, actual = new_prices)  # with actual future data
plotlambda(result)                    # Training RMSE bar chart
predictGINN(result, h = 12)          # variance forecast
AccuracyGINN(result, actual = new_prices)  # accuracy vs actual
plot(result, "loss")                  # training loss (unchanged)
accuracy.GINN(result)            # accuracy data frame

Dependencies

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.