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200 1 $a1900$eart at the crossroads$fby Robert Rosenblum and MaryAnne Stevens
210   $aNew York$aLondon$cHarry N. Abrams$d2000
215   $a448 p.$ccol. ill.$d31 cm.
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200 10$aComputational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk /$fby Fahed Mostafa, Tharam Dillon, Elizabeth Chang
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (X, 171 p. 23 illus.)
225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v697
311   $a3-319-51666-3 
327   $aCHAPTER 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.
330   $aThe results in this book demonstrate the power of neural networks in learning complex behavior from the underlying financial time series data . The results in this book also demonstrate how neural networks can successfully be applied to volatility modeling, option pricings, and value at risk modeling. These features allow them to be applied to market risk problems to overcome classical issues associated with statistical models. .
410  0$aStudies in Computational Intelligence,$x1860-949X ;$v697
606   $aComputational intelligence
606   $aArtificial intelligence
606   $aMacroeconomics
606   $aOperations research
606   $aDecision making
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
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615  0$aComputational intelligence.
615  0$aArtificial intelligence.
615  0$aMacroeconomics.
615  0$aOperations research.
615  0$aDecision making.
615 14$aComputational Intelligence.
615 24$aArtificial Intelligence.
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615 24$aOperations Research/Decision Theory.
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