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

Name: Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
SKU: 833d7900dabac246e21425810bd58776
Price: 69.43 EUR
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Bol Partner 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. 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. 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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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. 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. 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.

Lees meer