In order to improve the forecasting accuracy of the volatilities of the markets, we propose the hybrid models based on artificial neural networks with multi-hidden layers in this paper. Specifically, the hybrid models are built using the estimated volatilities obtained from GARCH family models and Google domestic trends (GDTs) as input variables. We further carry out many experiments varying the number of layers and activation functions to obtain the accurate hybrid model for forecasting volatility. The proposed models are applied to forecast weekly and monthly volatilities of S&P 500 index to verify their accuracy. The performance comparison results show that the hybrid models with GDTs outperform clearly the predicted results with GARCH family models and the hybrid models without GDTs in forecasting the volatility of actual market. We also provide the experiment results with graphs to illustrate the efficiency of models.
Bibliographical noteFunding Information:
We are grateful to the editor and two anonymous referees for detailed comments, which have materially improved the original version of the manuscript. Sungchul Lee is supported by the National Research Foundation of Korea grant funded by the Korea government (Grant No. NRF-2017R1A2B2005661). Geonwoo Kim is supported by the National Research Foundation of Korea grant funded by the Korea government (Grant No. NRF-2017R1E1A1A03070886).
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)