Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs
This addresses volatility prediction for investors in the Indian stock market, but it is incremental as it applies existing neural network methods to this specific context.
The paper tackled forecasting volatility in the Indian stock market by constructing Artificial Neural Network models using multiple inputs, including India VIX and global market volatilities, and found effectiveness judged through experiments over three time periods.
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.