TY  - BOOK
AU  - Sahoo,Amit 
AU  - Garg, Aditya 
AU  - Jangid, Jitendra 
AU  - Chandra. Abhijeet 
TI  - Improving the Volatility Forecasts of GARCH Family Models with the Recurrent Neural Networks
KW  - Volatility
KW  - GARCH standard models
KW  - GJR-GARCH model
KW  - Markov Switching GARCH
KW  - Artificial Neural Network (ANN)
KW  - RNN-GARCH
KW  - Conditional variance
N2  - The primary objective of this paper is to develop a family of GARCH models, combining them with popular Recurrent Neural Network (RNN) models, which can capture the high nonlinear relationships between past return innovations and conditional variance, which is overlooked by standard GARCH models. The next objective is to apply Markov Switching GARCH model and see the differences in the predictive accuracy (according to AIC and BIG criteria and in two viewpoints: MSB and MAD) between the standard GARCH models, GJRGARCH model, RNN-GARCH models and Markov Switching GARCH model by comparing their out-of-sample forecasts. The dataset consists of a series of daily returns obtained from the National Stock Exchange (NSE) for the Indian Equity Markets. The results indicated that the proposed RNN-GARCH model and RNN-Markov Switching models are accurate and quick prediction methods
ER  -