Screen LibraryComputational 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: | |
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| Corporate Authors: | |
| Group Author: | ; |
| Published: |
Springer International Publishing : Imprint: Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | 2017. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Studies in Computational Intelligence,
697 |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-319-51668-4 |
| Summary: |
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. . |
| Carrier Form: | 1 online resource (X, 171 pages): illustrations. |
| ISBN: | 9783319516684 |
| Index Number: | Q342 |
| CLC: | TP18 |
| 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. |