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Computation and simulation for finance = an introduction with Python /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computation and simulation for finance/ by Cónall Kelly.
Reminder of title:	an introduction with Python /
Author:	Kelly, Cónall.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xvi, 330 p. :ill. (chiefly col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Derivative securities - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-60575-8
ISBN:	9783031605758

Computation and simulation for finance = an introduction with Python /
Kelly, Cónall.

Computation and simulation for financean introduction with Python /[electronic resource] :by Cónall Kelly. - Cham :Springer International Publishing :2024. - xvi, 330 p. :ill. (chiefly col.), digital ;24 cm. - Springer undergraduate texts in mathematics and technology,1867-5514. - Springer undergraduate texts in mathematics and technology..

- 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.

ISBN: 9783031605758

Standard No.: 10.1007/978-3-031-60575-8doiSubjects--Topical Terms:

568178
Derivative securities
--Mathematical models.

LC Class. No.: HG6024

Dewey Class. No.: 332.645

Computation and simulation for finance = an introduction with Python /
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520 $a 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.
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