Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk

Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk by Fahed Mostafa, Tharam Dillon, Elizabeth Chang.

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 modelin...

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Bibliographic Details
Main Authors: Mostafa, Fahed (Author), Dillon, Tharam (Author), Chang, Elizabeth (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:Studies in Computational Intelligence, 697
Springer eBook Collection.
Subjects:
Computational intelligence.
Artificial intelligence.
Macroeconomics.
Operations research.
Decision making.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary: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. .
Physical Description:X, 171 p. 23 illus. online resource.
ISBN:9783319516684
ISSN:1860-949X ;
DOI:10.1007/978-3-319-51668-4

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