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

✍ Scribed by Fahed Mostafa, Tharam Dillon, Elizabeth Chang (auth.)

• Publisher: Springer International Publishing
• Year: 2017
• Tongue: English
• Leaves: 177
• Series: Studies in Computational Intelligence 697
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
Introduction....Pages 1-7
Time Series Modelling....Pages 9-30
Options and Options Pricing Models....Pages 31-49
Neural Networks and Financial Forecasting....Pages 51-80
Important Problems in Financial Forecasting....Pages 81-90
Volatility Forecasting....Pages 91-112
Option Pricing....Pages 113-135
Value-at-Risk....Pages 137-147
Conclusion and Discussion....Pages 149-158
Back Matter....Pages 159-171

✦ Subjects

Computational Intelligence;Artificial Intelligence (incl. Robotics);Macroeconomics/Monetary Economics//Financial Economics;Operation Research/Decision Theory