03985nam a22005175i 4500 979-8-8688-1052-7 DE-He213 20251030114618.0 cr nn 008mamaa 241214s2024 xxu| s |||| 0|eng d 9798868810527 979-8-8688-1052-7 10.1007/979-8-8688-1052-7 doi QA76.73.P98 UMX bicssc COM051360 bisacsh UMX thema Nag, Avishek. author. aut http://id.loc.gov/vocabulary/relators/aut 88949 Stochastic Finance with Python [electronic resource] : Design Financial Models from Probabilistic Perspective / by Avishek Nag. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XIV, 396 p. 103 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Part 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. Journey 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. Python (Computer program language). 64608 Business enterprises Finance. 58021 Financial engineering. 58071 Python. 47553 Corporate Finance. 57278 Financial Technology and Innovation. 69289 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868810510 Printed edition: 9798868810534 Printed edition: 9798868813474 https://doi.org/10.1007/979-8-8688-1052-7 ZDB-2-CWD ZDB-2-SXPC 65719 65719