LEADER 03838nam 22005775 450 001 9910917787203321 005 20251204105757.0 010 $a9798868810527 024 7 $a10.1007/979-8-8688-1052-7 035 $a(MiAaPQ)EBC31837002 035 $a(Au-PeEL)EBL31837002 035 $a(CKB)37018347600041 035 $a(DE-He213)979-8-8688-1052-7 035 $a(CaSebORM)9798868810527 035 $a(OCoLC)1482786753 035 $a(OCoLC-P)1482786753 035 $a(EXLCZ)9937018347600041 100 $a20241214d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStochastic Finance with Python $eDesign Financial Models from Probabilistic Perspective /$fby Avishek Nag 205 $a1st ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (398 pages) 311 08$a9798868810510 327 $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. 330 $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. 606 $aPython (Computer program language) 606 $aBusiness enterprises$xFinance 606 $aFinancial engineering 606 $aPython 606 $aCorporate Finance 606 $aFinancial Technology and Innovation 615 0$aPython (Computer program language) 615 0$aBusiness enterprises$xFinance. 615 0$aFinancial engineering. 615 14$aPython. 615 24$aCorporate Finance. 615 24$aFinancial Technology and Innovation. 676 $a005.133 700 $aNag$b Avishek$01780053 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910917787203321 996 $aStochastic Finance with Python$94303697 997 $aUNINA