From GARCH to Neural Network for Volatility Forecast
Pengfei Zhao Haoren Zhu Wilfred Siu Hung NG Dik Lun Lee
Volatility, as a measure of uncertainty, plays a crucial role in numerous
financial activities such as risk management. The Econometrics and Machine
Learning communities have developed two distinct approaches for financial
volatility forecasting: the stochastic approach and the neural network (NN)
approach. Despite their individual strengths, these methodologies have
conventionally evolved in separate research trajectories with little
interaction between them. This study endeavors to bridge this gap by
establishing an equivalence relationship between models of the GARCH family and
their corresponding NN counterparts. With the equivalence relationship
established, we introduce an innovative approach, named GARCH-NN, for
constructing NN-based volatility models. It obtains the NN counterparts of
GARCH models and integrates them as components into an established NN
architecture, thereby seamlessly infusing volatility stylized facts (SFs)
inherent in the GARCH models into the neural network. We develop the GARCH-LSTM
model to showcase the power of the GARCH-NN approach. Experiment results
validate that amalgamating the NN counterparts of the GARCH family models into
established NN models leads to enhanced outcomes compared to employing the
stochastic and NN models in isolation.