Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk, 1st ed. 2017 Studies in Computational Intelligence Series, Vol. 697
Langue : Anglais
Auteurs : Mostafa Fahed, Dillon Tharam, Chang Elizabeth
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.
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.
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
Date de parution : 05-2018
Ouvrage de 171 p.
15.5x23.5 cm
Date de parution : 03-2017
Ouvrage de 171 p.
15.5x23.5 cm
Thème de Computational Intelligence Applications to Option... :
© 2024 LAVOISIER S.A.S.