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

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  • © 2017

Overview

Authors:
  1. Fahed Mostafa

    1. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia

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  2. Tharam Dillon

    1. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia

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  3. Elizabeth Chang

    1. School of Business, University of New South Wales, Canberra, ACT, Australia

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  • 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)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 1-7

  3. Time Series Modelling

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 9-30

  4. Options and Options Pricing Models

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 31-49

  5. Neural Networks and Financial Forecasting

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 51-80

  6. Important Problems in Financial Forecasting

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 81-90

  7. Volatility Forecasting

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 91-112

  8. Option Pricing

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 113-135

  9. Value-at-Risk

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 137-147

  10. Conclusion and Discussion

    • Fahed Mostafa, Tharam Dillon, Elizabeth Chang
    Pages 149-158

  11. Back Matter

    Pages 159-171
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Keywords

About this book

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.
 


Reviews

“The book describes how to deal with the different sorts of financial market risk. … The book can be used by advanced undergraduate students and graduate students in its entirety. It is also interesting for the specialists in financial market risk and is of considerable importance to practitioners in the field.” (Yuliya S. Mishura, zbMath 1410.91004, 2019)

Authors and Affiliations

  • Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia

    Fahed Mostafa, Tharam Dillon

  • School of Business, University of New South Wales, Canberra, ACT, Australia

    Elizabeth Chang

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