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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 modeling, option pricings, and value at risk modelin...

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Main Authors: Mostafa, Fahed. (Author, http://id.loc.gov/vocabulary/relators/aut), Dillon, Tharam. (http://id.loc.gov/vocabulary/relators/aut), Chang, Elizabeth. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:Studies in Computational Intelligence, 697
Subjects:
Computational intelligence.
Artificial intelligence.
Macroeconomics.
Operations research.
Decision making.
Computational Intelligence.
Artificial Intelligence.
Macroeconomics/Monetary Economics//Financial Economics.
Operations Research/Decision Theory.
Online Access:https://doi.org/10.1007/978-3-319-51668-4
Table of Contents:
  • CHAPTER 1 Introduction
  • CHAPTER 2 Time Series Modelling
  • CHAPTER 3 Options and Options Pricing Models
  • CHAPTER 4 Neural Networks and Financial Forecasting
  • CHAPTER 5 Important Problems in Financial Forecasting
  • CHAPTER 6 Volatility Forecasting
  • CHAPTER 7 Option Pricing
  • CHAPTER 8 Value-at-Risk
  • CHAPTER 9 Conclusion and Discussion.

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