Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
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 modelin
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Fahed Mostafa Tharam Dillon Elizabeth Chang
Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
Studies in Computational Intelligence Volume 697
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
About this Series The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
Fahed Mostafa Tharam Dillon Elizabeth Chang •
Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
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Fahed Mostafa Department of Computer Science and Computer Engineering La Trobe University Bundoora, VIC Australia
Elizabeth Chang School of Business University of New South Wales Canberra, ACT Australia
Tharam Dillon Department of Computer Science and Computer Engineering La Trobe University Bundoora, VIC Australia
ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-319-51666-0 ISBN 978-3-319-51668-4 (eBook) DOI 10.1007/978-3-319-51668-4 Library of Congress Control Number: 2016960767 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or impli
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