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Computational 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...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
Springer
2017
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-51668-4 http://cds.cern.ch/record/2258657 |
| Sumario: | 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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