| Abstract: |
Volatility, which indicates the dispersion of returns, is a crucial measure of
risk and is hence used extensively for pricing and discriminating between
different financial investments. As a result, accurate volatility prediction
receives extensive attention. The Generalized Autoregressive Conditional
Heteroscedasticity (GARCH) model and its succeeding variants are well
established models for stock volatility forecasting. More recently, deep
learning models have gained popularity in volatility prediction as they
demonstrated promising accuracy in certain time series prediction tasks.
Inspired by Physics-Informed Neural Networks (PINN), we constructed a new,
hybrid Deep Learning model that combines the strengths of GARCH with the
flexibility of a Long Short-Term Memory (LSTM) Deep Neural Network (DNN), thus
capturing and forecasting market volatility more accurately than either class
of models are capable of on their own. We refer to this novel model as a
GARCH-Informed Neural Network (GINN). When compared to other time series
models, GINN showed superior out-of-sample prediction performance in terms of
the Coefficient of Determination ($R^2$), Mean Squared Error (MSE), and Mean
Absolute Error (MAE). |