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Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 120 - Number 8 |
| Year of Publication: 2015 |
| Authors: Tamal Datta Chaudhuri, Indranil Ghosh |
10.5120/21245-4034
|
Tamal Datta Chaudhuri, Indranil Ghosh . Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs. International Journal of Computer Applications. 120, 8 ( June 2015), 7-15. DOI=10.5120/21245-4034
Abstract
Volatility in stock markets has been extensively studied in the applied finance literature. In this paper, Artificial Neural Network models based on various back propagation algorithms have been constructed to predict volatility in the Indian stock market through volatility of NIFTY returns and volatility of gold returns. This model considers India VIX, CBOE VIX, volatility of crude oil returns (CRUDESDR), volatility of DJIA returns (DJIASDR), volatility of DAX returns (DAXSDR), volatility of Hang Seng returns (HANGSDR) and volatility of Nikkei returns (NIKKEISDR) as predictor variables. Three sets of experiments have been performed over three time periods to judge the effectiveness of the approach.
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