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100 _aVejendla, Ajitha
_922975
245 _aEvaluation of garch, rnn and fnn models for forecasting volatility in the financial markets.
_cAjitha Vejendla and David Enke,
260 _aHydrabad
_bIUP Publications
_cMarch 2013
500 _aolatility forecasting is an important task for those associated with the financial markets, and has occupied the attention of academics and practitioners over the last two decades. This research paper reflects the importance of volatility in option pricing, security valuation and risk management. It investigates the forecasting ability of Feed-Forward Neural Networks (FNN) using backpropagation learning, Recurrent Neural Networks (RNN), and a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model based on empirical price and historical volatility data. The performance of the three models is studied and compared using data of S&P 500, DJIA, NYSE and NASDAQ indexes. The results obtained from these selected models are anticipated to have significant market directional ability and lower prediction errors.
650 _aSecurities Market
_922977
650 _aVolaties Market
_922979
650 _aMarket Pricing
_922981
650 _aRisk Management in Business
_922983
650 _aNeural networks (Computer science)
_922985
650 _aGARCH model
_922987
700 _aEnke, David
_922989
773 0 _030419
_932180
_aMURTHY, E N
_dIUP PUBLICATION HYDEARABAD
_o555773
_tFINANCIAL RISK MANAGEMENT
_x0972-916X
942 _2ddc
_cJA-ARTICLE
999 _c43163
_d43163