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Record Nr. |
UNINA9910254335503321 |
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Autore |
Mostafa Fahed |
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Titolo |
Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk / / by Fahed Mostafa, Tharam Dillon, Elizabeth Chang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (X, 171 p. 23 illus.) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 697 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Macroeconomics |
Operations research |
Decision making |
Computational Intelligence |
Artificial Intelligence |
Macroeconomics/Monetary Economics//Financial Economics |
Operations Research/Decision Theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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CHAPTER 1 Introduction -- CHAPTER 2 Time Series Modelling -- CHAPTER 3 Options and Options Pricing Models -- CHAPTER 4 Neural Networks and Financial Forecasting -- CHAPTER 5 Important Problems in Financial Forecasting -- CHAPTER 6 Volatility Forecasting -- CHAPTER 7 Option Pricing -- CHAPTER 8 Value-at-Risk -- CHAPTER 9 Conclusion and Discussion. |
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Sommario/riassunto |
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The results in this book demonstrate the power of neural networks in learning complex behavior from the underlying financial time series data . The results in this book also demonstrate how neural networks can successfully be applied to volatility modeling, option pricings, and value at risk modeling. These features allow them to be applied to market risk problems to overcome classical issues associated with statistical models. . |
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