Abstract
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.
| Original language | English |
|---|---|
| Article number | 1950006 |
| Journal | Fluctuation and Noise Letters |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Mathematics(all)
- Physics and Astronomy(all)
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Forecasting the Volatility of Stock Market Index Using the Hybrid Models with Google Domestic Trends. / Seo, Monghwan; Lee, Sung chul; Kim, Geonwoo.
In: Fluctuation and Noise Letters, Vol. 18, No. 1, 1950006, 01.03.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Forecasting the Volatility of Stock Market Index Using the Hybrid Models with Google Domestic Trends
AU - Seo, Monghwan
AU - Lee, Sung chul
AU - Kim, Geonwoo
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056591396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056591396&partnerID=8YFLogxK
U2 - 10.1142/S0219477519500068
DO - 10.1142/S0219477519500068
M3 - Article
AN - SCOPUS:85056591396
VL - 18
JO - Fluctuation and Noise Letters
JF - Fluctuation and Noise Letters
SN - 0219-4775
IS - 1
M1 - 1950006
ER -