000 04430nam a22005415i 4500
001 978-3-642-33590-7
003 DE-He213
005 20140220082855.0
007 cr nn 008mamaa
008 130321s2013 gw | s |||| 0|eng d
020 _a9783642335907
_9978-3-642-33590-7
024 7 _a10.1007/978-3-642-33590-7
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aRüschendorf, Ludger.
_eauthor.
245 1 0 _aMathematical Risk Analysis
_h[electronic resource] :
_bDependence, Risk Bounds, Optimal Allocations and Portfolios /
_cby Ludger Rüschendorf.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXII, 408 p. 12 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Operations Research and Financial Engineering,
_x1431-8598
505 0 _aPreface.-Part I: Stochastic Dependence and Extremal Risk.-1 Copulas, Sklar's Theorem, and Distributional Transform -- 2 Fréchet Classes, Risk Bounds, and Duality Theory -- 3 Convex Order, Excess of Loss, and Comonotonicity -- 4 Bounds for the Distribution Function and Value at Risk of the Joint Portfolio -- 5 Restrictions on the Dependence Structure -- 6 Dependence Orderings of Risk Vectors and Portfolios -- Part II: Risk Measures and Worst Case Portfolios -- 7 Risk Measures for Real Risks -- 8 Risk Measures for Portfolio Vectors -- 9 Law Invariant Convex Risk Measures on L_d^p and Optimal Mass Transportation -- Part III: Optimal Risk Allocation -- 10 Optimal Allocations and Pareto Equilibrium -- 11 Characterization and Examples of Optimal Risk Allocations for Convex Risk Functionals -- 12 Optimal Contingent Claims and (Re)Insurance Contracts -- Part IV: Optimal Portfolios and Extreme Risks -- 13 Optimal Portfolio Diversification w.r.t. Extreme Risks -- 14 Ordering of Multivariate Risk Models with Respect to Extreme Portfolio Losses -- References -- List of Symbols -- Index.
520 _aThe author's particular interest in the area of risk measures is to combine this theory with the analysis of dependence properties. The present volume gives an introduction of basic concepts and methods in mathematical risk analysis, in particular of those parts of risk theory that are of special relevance to finance and insurance. Describing the influence of dependence in multivariate stochastic models on risk vectors is the main focus of the text that presents main ideas and methods as well as their relevance to practical applications. The first part introduces basic probabilistic tools and methods of distributional analysis, and describes their use to the modeling of dependence and to the derivation of risk bounds in these models. In the second, part risk measures with a particular focus on those in the financial and insurance context are presented. The final parts are then devoted to applications relevant to optimal risk allocation, optimal portfolio problems as well as to the optimization of insurance contracts. Good knowledge of basic probability and statistics as well as of basic general mathematics is a prerequisite for comfortably reading and working with the present volume, which is intended for graduate students, practitioners and researchers and can serve as a reference resource for the main concepts and techniques.  
650 0 _aMathematics.
650 0 _aFinance.
650 0 _aDistribution (Probability theory).
650 0 _aEconomics
_xStatistics.
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aQuantitative Finance.
650 2 4 _aActuarial Sciences.
650 2 4 _aApplications of Mathematics.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642335891
830 0 _aSpringer Series in Operations Research and Financial Engineering,
_x1431-8598
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-33590-7
912 _aZDB-2-SMA
999 _c97395
_d97395