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_aVejendla, Ajitha _922975 |
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_aEvaluation of garch, rnn and fnn models for forecasting volatility in the financial markets. _cAjitha Vejendla and David Enke, |
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_aHydrabad _bIUP Publications _cMarch 2013 |
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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. | ||
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_aSecurities Market _922977 |
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_aVolaties Market _922979 |
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_aMarket Pricing _922981 |
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_aRisk Management in Business _922983 |
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_aNeural networks (Computer science) _922985 |
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_aGARCH model _922987 |
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_aEnke, David _922989 |
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_030419 _932180 _aMURTHY, E N _dIUP PUBLICATION HYDEARABAD _o555773 _tFINANCIAL RISK MANAGEMENT _x0972-916X |
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_2ddc _cJA-ARTICLE |
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_c43163 _d43163 |