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

Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk: Studies in Computational Intelligence, cartea 697

Autor Fahed Mostafa, Tharam Dillon, Elizabeth Chang
en Limba Engleză Hardback – 10 mar 2017
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.
 

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Specificații

ISBN-13: 9783319516660
ISBN-10: 3319516663
Pagini: 171
Ilustrații: X, 171 p. 23 illus.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.44 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

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.

Recenzii

“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)

Textul de pe ultima copertă

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. 

Caracteristici

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