Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk [electronic resource] / by Fahed Mostafa, Tharam Dillon, Elizabeth Chang.
By: Mostafa, Fahed [author.]
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Contributor(s): Dillon, Tharam [author.] | Chang, Elizabeth [author.] | SpringerLink (Online service).
Material type: 
BookSeries: Studies in Computational Intelligence: 697Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: X, 171 p. 23 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319516684.Subject(s): Computational intelligence | Artificial intelligence | Macroeconomics | Operations research | Computational Intelligence | Artificial Intelligence | Macroeconomics and Monetary Economics | Operations Research and Decision TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online 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.
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. .
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