Authors:
- Presents an in-depth analysis of neural-network research in financial time series
- Addresses various issues concerning neural network modeling in market risk
- Explains and demonstrates how neural networks can overcome shortcomings in statistical time series modeling
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 697)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
Reviews
Authors and Affiliations
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Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia
Fahed Mostafa, Tharam Dillon
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School of Business, University of New South Wales, Canberra, ACT, Australia
Elizabeth Chang
Bibliographic Information
Book Title: Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
Authors: Fahed Mostafa, Tharam Dillon, Elizabeth Chang
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-51668-4
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-51666-0Published: 10 March 2017
Softcover ISBN: 978-3-319-84713-9Published: 04 May 2018
eBook ISBN: 978-3-319-51668-4Published: 28 February 2017
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 171
Number of Illustrations: 23 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Macroeconomics/Monetary Economics//Financial Economics, Operations Research/Decision Theory