04573nam 22006615 450 991087467900332120251217135726.09783031605758(electronic bk.)978303160574110.1007/978-3-031-60575-8(MiAaPQ)EBC31534705(Au-PeEL)EBL31534705(CKB)33030951200041(DE-He213)978-3-031-60575-8(EXLCZ)993303095120004120240719d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputation and Simulation for Finance An Introduction with Python /by Cónall Kelly1st ed. 2024.Cham :Springer International Publishing :Imprint: Springer,2024.1 online resource (335 pages)Springer Undergraduate Texts in Mathematics and Technology,1867-5514Print version: Kelly, Cónall Computation and Simulation for Finance Cham : Springer International Publishing AG,c2024 9783031605741 Includes bibliographical references and index.- 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.This 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.Springer Undergraduate Texts in Mathematics and Technology,1867-5514Social sciencesMathematicsMathematicsData processingMathematics in Business, Economics and FinanceComputational Mathematics and Numerical AnalysisPython (Llenguatge de programació)thubAnàlisi numèricathubEquacions diferencials estocàstiquesthubActius financers derivatsthubMatemàtica financerathubLlibres electrònicsthubSocial sciencesMathematics.MathematicsData processing.Mathematics in Business, Economics and Finance.Computational Mathematics and Numerical Analysis.Python (Llenguatge de programació)Anàlisi numèricaEquacions diferencials estocàstiquesActius financers derivatsMatemàtica financera332.6457Kelly Cónall1749271MiAaPQMiAaPQMiAaPQ9910874679003321Computation and Simulation for Finance4183280UNINA