000 03318nam a22005775i 4500
999 _c12404
_d12404
001 978-3-319-51668-4
003 DE-He213
005 20211203094559.0
008 170302s2017 gw | s |||| 0|eng d
020 _a9783319516684
040 _cAIKTC-KRRC
041 _aENG
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aMostafa, Fahed.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aComputational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk
_h[electronic resource] /
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aX, 171 p. 23 illus.
_bCard Paper
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v697
520 _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. .
650 0 _aComputer Engineering
_94622
653 _aArtificial Intelligence.
653 _aMacroeconomics/Monetary Economics//Financial Economics.
653 _aOperations Research/Decision Theory.
700 1 _aDillon, Tharam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aChang, Elizabeth.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319516660
776 0 8 _iPrinted edition:
_z9783319516677
776 0 8 _iPrinted edition:
_z9783319847139
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v697
856 4 0 _uhttps://doi.org/10.1007/978-3-319-51668-4
_zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942 _cEBOOKS
_2ddc