Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk [electronic resource] /
By: Mostafa, Fahed [author.].
Contributor(s): Dillon, Tharam [author.] | Chang, Elizabeth [author.] | SpringerLink (Online service).
Series: Studies in Computational Intelligence: 697Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: X, 171 p. 23 illus. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319516684.Subject(s): Computer Engineering
| Artificial Intelligence | Macroeconomics/Monetary Economics//Financial Economics | Operations Research/Decision TheoryDDC classification: 006.3 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: 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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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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