CAN DEEP LEARNING BEAT TRADITIONAL ECONOMETRICS IN FORECASTING OF REALIZED VOLATILITY?

Volatility modelling is a field dominated by classic Econometric methods such as the Nobel Prize winning Autoregressive conditional heteroskedasticity (ARCH) model. This paper therefore investigates if the field of Deep Learning can live up to the hype and outperform classic Econometrics in forecasting of realized volatility. By letting the Heterogeneous AutoRegressive model of Realized Volatility with multiple jump components (HAR-RV-CJ) represent the Econometric field as benchmark model, we compare its efficiency in forecasting realized volatility to four Deep Learning models. The results of the experiment show that the HAR-RV-CJ performs in line with the four Deep Learning models: Feed Forward Neural Network (FNN), Recurrent Neural Network (RNN), Long Short Term Memory network (LSTM) and Gated Recurrent Unit Network (GRU). Hence, the paper cannot conclude that the field of Deep Learning is superior to classic Econometrics in forecasting of realized volatility