000			01721nam a2200265 4500
999			_c49745 _d49745
003			OSt
005			20180511151359.0
008			180511b ||||| |||| 00| 0 eng d
100			_aSahoo,Amit _931446
245			_aImproving the Volatility Forecasts of GARCH Family Models with the Recurrent Neural Networks
300			_a33-50 p.
520			_aThe 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.
653			_aVolatility
653			_aGARCH standard models
653			_aGJR-GARCH model
653			_aMarkov Switching GARCH
653			_aArtificial Neural Network (ANN)
653			_aRNN-GARCH
653			_aConditional variance
700			_aGarg, Aditya _931447
700			_aJangid, Jitendra _931448
700			_aChandra. Abhijeet _931449
773	0		_030426 _970141 _aMURTHY, E N _dIUP PUBLICATION HYDERABAD _o5558566 _tCOMPUTER SCIENCES
942			_2ddc _cJA-ARTICLE