LEADER 04058nam 22005295 450 001 9910874679003321 005 20250807132305.0 010 $a9783031605758$b(electronic bk.) 010 $z9783031605741 024 7 $a10.1007/978-3-031-60575-8 035 $a(MiAaPQ)EBC31534705 035 $a(Au-PeEL)EBL31534705 035 $a(CKB)33030951200041 035 $a(DE-He213)978-3-031-60575-8 035 $a(EXLCZ)9933030951200041 100 $a20240719d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputation and Simulation for Finance $eAn Introduction with Python /$fby Cónall Kelly 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (335 pages) 225 1 $aSpringer Undergraduate Texts in Mathematics and Technology,$x1867-5514 311 08$aPrint version: Kelly, Cónall Computation and Simulation for Finance Cham : Springer International Publishing AG,c2024 9783031605741 320 $aIncludes bibliographical references and index. 327 $a- Part I Modelling Assets and Markets -- Introduction -- The Pricing of Financial Derivatives -- Part II Computational Pricing Methods in the Black-Scholes Framework -- Binomial Tree Methods -- Simulation I: Monte Carlo Methods -- Finite Difference Methods -- Part III Simulation Methods Beyond the Black-Scholes Framework -- Simulation II: Modelling Multivariate Financial Data -- Stochastic Models for Interest Rates -- Simulation III: Numerical Approximation of SDE Models. 330 $aThis book offers an up-to-date introductory treatment of computational techniques applied to problems in finance, placing issues such as numerical stability, convergence and error analysis in both deterministic and stochastic settings at its core. The first part provides a welcoming but nonetheless rigorous introduction to the fundamental theory of option pricing, including European, American, and exotic options along with their hedge parameters, and combines a clear treatment of the mathematical framework with practical worked examples in Python. The second part explores the main computational methods for valuing options within the Black-Scholes framework: lattice, Monte Carlo, and finite difference methods. The third and final part covers advanced topics for the simulation of financial processes beyond the standard Black-Scholes setting. Techniques for the analysis and simulation of multidimensional financial data, including copulas, are covered and will be of interest to those studying machine learning for finance. There is also an in-depth treatment of exact and approximate sampling methods for stochastic differential equation models of interest rates and volatilities. Written for advanced undergraduate and masters-level courses, the book assumes some exposure to core mathematical topics such as linear algebra, ordinary differential equations, multivariate calculus, probability, and statistics at an undergraduate level. While familiarity with Python is not required, readers should be comfortable with basic programming constructs such as variables, loops, and conditional statements. 410 0$aSpringer Undergraduate Texts in Mathematics and Technology,$x1867-5514 606 $aSocial sciences$xMathematics 606 $aMathematics$xData processing 606 $aMathematics in Business, Economics and Finance 606 $aComputational Mathematics and Numerical Analysis 615 0$aSocial sciences$xMathematics. 615 0$aMathematics$xData processing. 615 14$aMathematics in Business, Economics and Finance. 615 24$aComputational Mathematics and Numerical Analysis. 676 $a332.6457 700 $aKelly$b Co?nall$01749271 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910874679003321 996 $aComputation and Simulation for Finance$94183280 997 $aUNINA