000 03362nam a22004935i 4500
001 978-3-540-30591-0
003 DE-He213
005 20160302161729.0
007 cr nn 008mamaa
008 100301s2005 gw | s |||| 0|eng d
020 _a9783540305910
_9978-3-540-30591-0
024 7 _a10.1007/3-540-30591-2
_2doi
050 4 _aHG1-HG9999
072 7 _aKFF
_2bicssc
072 7 _aBUS027000
_2bisacsh
082 0 4 _a332
_223
100 1 _aFengler, Matthias R.
_eauthor.
245 1 0 _aSemiparametric Modeling of Implied Volatility
_h[electronic resource] /
_cby Matthias R. Fengler.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2005.
300 _aXVI, 224 p. 61 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Finance
505 0 _aThe Implied Volatility Surface -- Smile Consistent Volatility Models -- Smoothing Techniques -- Dimension-Reduced Modeling -- Conclusion and Outlook.
520 _aThe implied volatility surface is a key financial variable for the pricing and the risk management of plain vanilla and exotic options portfolios alike. Consequently, statistical models of the implied volatility surface are of immediate importance in practice: they may appear as estimates of the current surface or as fully specified dynamic models describing its propagation through space and time. This book fills a gap in the financial literature by bringing together both recent advances in the theory of implied volatility and refined semiparametric estimation strategies and dimension reduction methods for functional surfaces: the first part of the book is devoted to smile-consistent pricing appoaches. The theory of implied and local volatility is presented concisely, and vital smile-consistent modeling approaches such as implied trees, mixture diffusion, or stochastic implied volatility models are discussed in detail. The second part of the book familiarizes the reader with estimation techniques that are natural candidates to meet the challenges in implied volatility modeling, such as the rich functional structure of observed implied volatility surfaces and the necessity for dimension reduction: non- and semiparametric smoothing techniques. The book introduces Nadaraya-Watson, local polynomial and least squares kernel smoothing, and dimension reduction methods such as common principle components, functional principle components models and dynamic semiparametric factor models. Throughout, most methods are illustrated with empirical investigations, simulations and pictures.
650 0 _aFinance.
650 0 _aEconomics, Mathematical.
650 0 _aMathematical models.
650 0 _aStatistics.
650 1 4 _aFinance.
650 2 4 _aFinance, general.
650 2 4 _aMathematical Modeling and Industrial Mathematics.
650 2 4 _aQuantitative Finance.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540262343
830 0 _aSpringer Finance
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-30591-2
912 _aZDB-2-SMA
999 _c174209
_d174209