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020 _a9783031605758
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024 7 _a10.1007/978-3-031-60575-8
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100 1 _aKelly, Cónall.
_eauthor.
_0(orcid)0000-0002-9707-2822
_1https://orcid.org/0000-0002-9707-2822
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980878
245 1 0 _aComputation and Simulation for Finance
_h[electronic resource] :
_bAn Introduction with Python /
_cby Cónall Kelly.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVI, 330 p. 69 illus., 61 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aSpringer Undergraduate Texts in Mathematics and Technology,
_x1867-5514
505 0 _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.
520 _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.
650 0 _aSocial sciences
_xMathematics.
_969736
650 0 _aMathematics
_xData processing.
_965715
650 1 4 _aMathematics in Business, Economics and Finance.
_969739
650 2 4 _aComputational Mathematics and Numerical Analysis.
_966073
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031605741
776 0 8 _iPrinted edition:
_z9783031605765
776 0 8 _iPrinted edition:
_z9783031605772
830 0 _aSpringer Undergraduate Texts in Mathematics and Technology,
_x1867-5514
_980879
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60575-8
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
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