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Stochastic Finance with Python [electronic resource] : Design Financial Models from Probabilistic Perspective / by Avishek Nag.

By: Contributor(s): Material type: TextPublisher: Berkeley, CA : Apress : Imprint: Apress, 2024Edition: 1st ed. 2024Description: XIV, 396 p. 103 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9798868810527
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • QA76.73.P98
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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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.

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