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020 _a9798868810527
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024 7 _a10.1007/979-8-8688-1052-7
_2doi
050 4 _aQA76.73.P98
072 7 _aUMX
_2bicssc
072 7 _aCOM051360
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072 7 _aUMX
_2thema
100 1 _aNag, Avishek.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988949
245 1 0 _aStochastic Finance with Python
_h[electronic resource] :
_bDesign Financial Models from Probabilistic Perspective /
_cby Avishek Nag.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXIV, 396 p. 103 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart I - Foundations & Pre-requisites -- Chapter 1 - Introduction -- Chapter 2 – Finance Basics & Data Sources -- Chapter 3 - Probability -- Chapter 4 - Simulation -- Chapter 5 – Stochastic Process -- Part II – Basic Asset Price Modelling -- Chapter 6 – Diffusion Model -- Chapter 7 – Jump Models -- Part III – Financial Options Modelling -- Chapter 8 – Options & Black-Scholes Model -- Chapter 9 – PDE, Finite-Difference & Black-Scholes Model -- Part IV - Portfolios -- Chapter 10 – Portfolio Optimization.
520 _aJourney through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python. The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.
650 0 _aPython (Computer program language).
_964608
650 0 _aBusiness enterprises
_xFinance.
_958021
650 0 _aFinancial engineering.
_958071
650 1 4 _aPython.
_947553
650 2 4 _aCorporate Finance.
_957278
650 2 4 _aFinancial Technology and Innovation.
_969289
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868810510
776 0 8 _iPrinted edition:
_z9798868810534
776 0 8 _iPrinted edition:
_z9798868813474
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-1052-7
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
999 _c65719
_d65719