Stochastic Finance with Python - Nag, Avishek; - Prospero Internet Bookshop

Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective
 
Product details:

ISBN13:9798868810510
ISBN10:8868810514
Binding:Paperback
No. of pages:396 pages
Size:254x178 mm
Language:English
Illustrations: 103 Illustrations, black & white
700
Category:

Stochastic Finance with Python

Design Financial Models from Probabilistic Perspective
 
Edition number: First Edition
Publisher: Apress
Date of Publication:
Number of Volumes: 1 pieces, Book
 
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  Piece(s)

 
Short description:

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.

Long description:

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.



What You Will Learn




  • Understand applied probability and statistics with finance

  • Design forecasting models of the stock price with the stochastic process, Monte-Carlo simulation.

  • Option price estimation with both risk-neutral probabilistic and PDE-driven approach.

  • Use Object-oriented Python to design financial models with reusability.



Who This Book Is For 



Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance



 



 

Table of 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.