04227nam a22005415i 4500 978-3-031-60575-8 DE-He213 20251030112637.0 cr nn 008mamaa 240719s2024 sz | s |||| 0|eng d 9783031605758 978-3-031-60575-8 10.1007/978-3-031-60575-8 doi H61.25 PBW bicssc K bicssc MAT003000 bisacsh PBW thema K thema Kelly, Cónall. author. (orcid)0000-0002-9707-2822 https://orcid.org/0000-0002-9707-2822 aut http://id.loc.gov/vocabulary/relators/aut 80878 Computation and Simulation for Finance [electronic resource] : An Introduction with Python / by Cónall Kelly. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVI, 330 p. 69 illus., 61 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Undergraduate Texts in Mathematics and Technology, 1867-5514 - 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. Social sciences Mathematics. 69736 Mathematics Data processing. 65715 Mathematics in Business, Economics and Finance. 69739 Computational Mathematics and Numerical Analysis. 66073 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031605741 Printed edition: 9783031605765 Printed edition: 9783031605772 Springer Undergraduate Texts in Mathematics and Technology, 1867-5514 80879 https://doi.org/10.1007/978-3-031-60575-8 ZDB-2-SMA ZDB-2-SXMS 61735 61735