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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk (Studies in Computational Intelligence, 697) - Hardcover
Synopsis
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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From the Back Cover
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.
Review
“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)
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- PublisherSpringer
- Publication date2017
- ISBN 10 3319516663
- ISBN 13 9783319516660
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages184
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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk (Studies in Computational Intelligence, 697)
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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
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Springer International Publishing, Springer Nature Switzerland Mär 2017, 2017
ISBN 10: 3319516663
ISBN 13: 9783319516660
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 184 pp. Englisch. Seller Inventory # 9783319516660
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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
Published by
Springer International Publishing, 2017
ISBN 10: 3319516663
ISBN 13: 9783319516660
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents an in-depth analysis of neural-network research in financial time seriesAddresses various issues concerning neural network modeling in market riskExplains and demonstrates how neural net. Seller Inventory # 133891075
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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
Published by
Springer International Publishing, Springer Nature Switzerland, 2017
ISBN 10: 3319516663
ISBN 13: 9783319516660
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783319516660
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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
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Springer-Verlag New York Inc, 2017
ISBN 10: 3319516663
ISBN 13: 9783319516660
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Hardcover
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Hardcover. Condition: Brand New. 171 pages. 9.00x6.00x0.50 inches. In Stock. Seller Inventory # x-3319516663
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