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

  • Fahed Mostafa
  • Tharam Dillon
  • Elizabeth Chang

Part of the Studies in Computational Intelligence book series (SCI, volume 697)

Table of contents

  1. Front Matter
    Pages i-x
  2. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 1-7
  3. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 9-30
  4. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 31-49
  5. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 51-80
  6. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 81-90
  7. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 91-112
  8. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 113-135
  9. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 137-147
  10. Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 149-158
  11. Back Matter
    Pages 159-171

About this book

Introduction

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. 

Keywords

Computational Intelligence Hedging Market Risks Neural Networks Option Pricing Model Value at Risk Volatility Models

Authors and affiliations

  • Fahed Mostafa
    • 1
  • Tharam Dillon
    • 2
  • Elizabeth Chang
    • 3
  1. 1.Department of Computer Science and Computer EngineeringLa Trobe UniversityBundooraAustralia
  2. 2.Department of Computer Science and Computer EngineeringLa Trobe UniversityBundooraAustralia
  3. 3.School of BusinessUniversity of New South WalesCanberra, ACTAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-51668-4
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-51666-0
  • Online ISBN 978-3-319-51668-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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